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2026-10-13 PENG 深度研究:内存增长、现金回收与稀释后的回报 ​

  • 数据时间:2026-10-13 美股盘中;Yahoo 指示报价 80.1624 美元,最后成交标记为 09:54:49 EDT。
  • 研究对象:Penguin Solutions(PENG);评估未来 3–12 个月的价格回报与基本面验证。
  • 报告类型:个股投资研究;金额除特别标注外为美元,财务表以百万美元计。
  • 资料来源:SEC、公司电话会及产品资料、Yahoo、StockAnalysis/S&P Global、TrendForce、Microsoft Research、USENIX。

结论与建议 ​

PENG 的内存增长已经兑现为收入,但当前约 80 美元尚未提供足够有利的风险回报;本期动作是 pass,不新增参与。 FY2026 第四季度内存贡献了公司约 91% 的同比收入增量,增长有实绩支撑。与此同时,全年经营现金流为 −151.9 百万美元,业务扩张需要投入大量营运资本。股价按 FY2027 公司调整后 EPS 指引计算约 18.0 倍,已要求增长延续与现金状况改善。财务与指引

我把可争取的超额回报来源放在三件事:固定价格下的内存出货扩张、增长中的现金回收、CXL 产品形成可量化利润。当前前两项缺少完整拆分,第三项只有商业化起点,尚无独立收入规模。因而“AI 内存需求强”足以支持继续跟踪,尚不足以支持在现价提高风险预算。

项目当前判断
最高优先级观点增长确定性高于现金兑现确定性;现价新增资金的补偿不足。
市场隐含判断我的价格解释是:市场已在计入 FY2027 约 40% 收入增长,并给 FY2028 调整后 EPS 共识约 15.24 倍估值。现金转正也已在聚合预期中。
不同判断市场容易把内存总收入增长与 CXL 成长合并定价;现有收入主体仍是定制模组,商品价格与营运资本会显著改变每股回报。
传导机制内存采购价及出货增加 → 收入、毛利额增加 → 库存和应收吸收现金 → 融资与股数影响每股收益;现金释放才能提高估值质量。
观点状态与证据变化新建深度判断。支持项:Q4 内存收入 +157.9%、FY2027 指引上调;反对项:全年营运资本吸收 374.0 百万、当前普通股数缺完整桥。对经营增长的信心高于对现金兑现和现价回报的信心。
当前首选动作pass/不新增参与;未知现有持仓,不提出个人账户调仓数量。
置信度中等。财务金额和证券条款较清楚,量价拆分、CXL 规模、现金回收与当前股数仍有关键缺口。
主要依据内存贡献 Q4 增收 91.2%;FY2026 CFO −151.9 百万;80.1624 美元对应 FY2027 指引 EPS 18.0 倍。
最大反证若公司在继续高增长时迅速回收资金,并披露 CXL 已构成实质利润贡献,较高估值可以持续,pass 会错过上涨。
失效条件任一触发即重做当前判断:①价格降至 72 美元或以下且盈利依据未恶化;②公司上调 FY2027 调整后 EPS 指引中点至至少 5.00 美元。触发只启动重新定价,仍须检查现金和股数。
下一验证FY2026 10-K 提交后核对期末股数、债务与客户资金;下一季业绩核对库存、CFO、内存价格/量及 CXL 收入。下一次公告日期本次未核实。
条件式框架估值折价或盈利上修先满足其一,再以连续两季累计 CFO 转正、没有靠应付款继续加速堆高为参与条件;订单取消或指引下调则撤回参与路径。

以上价格和财务阈值是本研究的决策条件,不是公司承诺,也不是自动交易规则。“Alpha”在这里指有依据的预期超额回报机会;本报告没有建立因子调整回测,不能宣称统计意义上的 alpha 已得到验证。

公司靠什么赚钱 ​

Penguin 把业务分为 Advanced Computing、Integrated Memory 和 Optimized LED。投资主线在前两项,但三者的经济特征差别很大。公司业务与财务

业务客户购买什么:示意例子回报来自哪里主要约束
Integrated Memory服务器 OEM 购买经过设计、验证的定制内存模组;部分客户采用代采物流服务;新产品包括 CXL 扩展卡与 MemoryAI。定制与验证能力、供货组织、客户项目延续;量价共同影响收入。芯片供应、价格周期、客户集中度、库存与应收资金。
Advanced Computing企业或 neocloud 让 Penguin 设计、部署并运维 GPU 集群,配合 ClusterWareAI 软件。系统交付、软件和运维服务、项目扩展。大项目确认节奏、客户资金和部署能力;软件与硬件各自收入占比未单列。
Optimized LED照明等客户采购 LED 产品。器件设计、制造与产品组合。增长较缓、竞争和产品周期,对当前 AI 投资主线贡献有限。

例如,服务器厂商把“特定容量、寿命与可靠性要求”交给 Penguin,Penguin 从上游采购存储器件、完成模组和验证,再按交付合同收款。这使公司能受益于数据中心内存需求,也让它承担供应链资金压力。FY2025 10-K 列出 Samsung、Micron、SK hynix、IBM、Kioxia 等供应商,其中一些同时具备模组竞争能力。历史业务与风险披露

好生意:经营杠杆有进展,现金资本强度偏高 ​

FY2026 GAAP 营业利润由 58.1 百万增至 165.6 百万,调整后营业利润由 167.7 百万增至 241.0 百万。增量收入带来了经营杠杆;但是低资本开支只说明固定资产投入较少,库存与应收仍需真实资金。评价这门生意,应同时看营业利润和营运资本回报。

好团队:先给执行进展信用,资本配置继续验收 ​

管理层改善了增长和营业利润,也在 Q4 完成长期零息融资、偿还 100 百万信用额度。需要继续核对的是:融资换来的流动性,能否最终变成可分配现金。FY2026 普通股回购现金支出 74.4 百万,与大额经营现金流流出同期发生;随后旧债交换又增发约 8.7 百万股。这一组合说明股东必须按净股数和每股现金评价资本配置,而不能只看回购金额。现金流表

Stephen Cumming 于 10 月 6 日出任 CFO。公开 Form 4 确认 33,502 个 RSU;公司后续稿披露另有 48,818 个目标 PSU,按三年相对 TSR 可兑现 0%–200%。RSU 与 PSU 目标合计 82,320 个单位,最高 131,138 个,需满足归属条件;这只是一个奖酬项目,整个公司的 SBC 和证券转换才是股数分析重点。任命 8-K、Form 4、10 月 12 日公司公告转载

内存增长:增长成立,结构性份额还需拆开 ​

谁贡献了增量 ​

业务FY2026 收入全年同比Q4 收入Q4 同比Q4 收入占比Q4 同比增收贡献
Integrated Memory924.0+99.0%340.8+157.9%60.1%91.2%
Advanced Computing558.8−13.8%154.0+11.4%27.2%6.9%
Optimized LED248.7−2.9%71.9+6.6%12.7%1.9%
公司合计1,731.5+26.5%566.7+67.7%100.0%100.0%

来源:FY2026 业绩公告原表;增收贡献=本分部同比增量/全公司同比增量。原始财务表

Q4 增长高度集中在内存。Advanced Computing 第四季度恢复增长,但全年仍下降,未来 AI 项目加速属于接下来要验证的收入。公告中客户拥有的 30 亿或 100 亿美元合同,是客户自己的合同规模,并未披露为 Penguin 的订单金额。

量、价与产品组合 ​

Q3 电话会明确说,大多数内存业务仍是 OEM 数据中心使用的定制模组;MemoryAI appliance 和 CXL cards 已有收入与新增 bookings,但未披露金额。Q4 管理层说量价均增,并给出 FY2027 内存收入增长中点约 50%、上下 10 个百分点的展望。关于此前量价接近均分的问答,涉及未交付 backlog 的假设,缺少可复算的金额桥。Q3 电话会、Q4 电话会

用最简单的“价格 × 数量/组合”恒等式,可以看清验证压力。以下只是敏感性,不是对公司实际量价的估算:

假设全年平均售价变化达到 50% 收入增长所需的数量/组合增幅
0%50.0%
+15%30.4%
+30%15.4%
+50%0.0%

计算为 1.50/(1+售价增幅)−1。代理收入、产品结构和会计净额确认会使真实桥更复杂。这个表说明:当价格涨幅放缓,维持同样的收入增长就更依赖交付量和高价值产品占比。

TrendForce 对日历年 2026 年第四季度的预测是常规 DRAM 合同价环比上涨 10%–15%,供给仍紧,但涨幅放缓;长期协议也会影响涨价传导。这支持短期需求与定价环境,同时提示 FY2027 不宜机械外推 FY2026 后半年的价格增速。PENG 的实际采购价、销售价和合同周期仍需单独核对。TrendForce 行业研究

Backlog 提高可见性,但还缺合同质量 ​

Q4 CEO 表示,内存 backlog 高于当季确认收入,并延伸至少四个季度。新增客户及扩展客户也提供了需求广度的信号。这里最重要的下一层证据是:订单金额、取消比例、交期、价格重置方式和预付款覆盖。FY2025 10-K 描述,多数客户预测不具约束性,客户一般可取消或延迟;本期没有逐项证明新增 backlog 已全部变成不可取消合同。Q4 电话会、10-K 订单风险

CXL:商业化已开始,估值贡献暂不单列 ​

CXL 允许系统扩展内存容量,MemoryAI 的用途包括卸载和复用推理过程中的 KV cache。当前产品目录提供最高 11TB 内存配置与询价入口;Q3 电话会确认产品收入和 bookings。这已经超过单纯概念展示,但单列收入、毛利、付费客户规模和复购仍缺失。产品目录、Q3 商业化描述

合作方 Astera 的演示说明了 CXL/RDMA 共享缓存的可行方向,但属于同一商业合作链条,未给出足以复现实验的完整条件。Microsoft Research 的公开论文摘要强调 CXL 扩展可行、生态部署需要逐步推进;USENIX 的研究摘要则指出 CXL 相对本地内存有延迟代价。这些资料支持技术需求存在,也表明实际收益依赖工作负载和系统设计。Astera 技术说明、Microsoft Research、USENIX

我在基准估值中不给 CXL 另加独立业务价值,避免把已经包含在公司或分析师盈利预测里的增长再算一次。上行情景允许产品组合改善和更高估值,但需要公司披露足以证明经济贡献的数据。

竞争优势放在哪里 ​

可保留的优势假设是定制设计、验证、固件/系统配合、长期客户项目和供应链服务。对手包括上游存储厂商自己的模组业务及其他模组供应商;CXL 的控制器、模组、软件与整机也有多个参与者。Penguin 的产品推出与客户合作可以缩短采购部署过程,但现有材料不足以证明独占架构或不可替代的专利壁垒。

FY2025 前十大客户贡献 66% 收入,其中一个内存客户贡献 14.1%;这是历史集中度。新增客户数量增加后,仍需查看 FY2026 收入是否真正分散。同行比较因此采取商业模式比较:MU 等上游厂商更直接承受晶圆资本与存储价格周期,PENG 更直接承受模组/项目交付及营运资本压力;本次没有收集同日同口径同行估值,故不据此声称“相对同行便宜”。历史客户与竞争披露

现金流:利润增长为何没有留下现金 ​

从净利润到 CFO 的完整桥 ​

项目(百万美元)FY2026Q4 FY2026
合并净利润,含非控股权益185.78895.009
折旧摊销+50.176+12.299
股权激励+31.815+6.620
旧债转换诱导费用+33.248+33.248
递延所得税调整−62.660−62.981
投资处置收益剔除−30.942−0.014
非上市投资减值+10.0000.000
债务发行成本摊销及其他+4.697+2.279
营运资本变化前经营现金222.12286.460
应收账款增加−488.360−91.995
存货增加−493.603−250.467
其他资产变化+0.964−8.935
应付、应计及其他负债增加+606.963+101.801
经营现金流 CFO−151.914−163.136
资本开支与设备定金−11.569−4.272
CFO 减资本开支−163.483−167.408

来源:SEC 现金流表。末行是简单 FCF 口径,尚未扣优先股现金分红。Q4 归属公司净利润是 93.204 百万,现金流表起点则是含非控股权益的 95.009 百万。

全年应收与库存合计吸收约 982.0 百万,供应商和其他经营负债提供约 607.0 百万缓冲,净营运资本仍吸收 374.0 百万。Q4 单季又吸收 249.6 百万。当前现金压力主要来自增长的资金占用,尚不能仅凭这些数据断言坏账或库存损失;同样,也缺少它会自动全部释放的证据。

税务也影响了利润观感:Q4 利润表含 57.6 百万所得税收益,现金桥有 −63.0 百万递延税调整。评价持续盈利更适合同时参照公司的正常化税率与调整后 EPS,并核对未来现金税负。

净收入与总开票金额的差别 ​

部分 memory logistics 按代理人模式确认收入:转售成本不进入 GAAP 收入和成本,但交易对应的资金仍在资产负债表流转。Q3 10-Q 披露代理转售开票成本 742.397 百万,加上净收入 478.713 百万,得到总开票金额约 1,221.110 百万。Q3 10-Q

Q4 管理层口述 gross sales 约 1,570 百万、gross COGS 约 1,420 百万,并以该口径算周转天数:DSO 46 天、库存 48 天、DPO 52 天,现金转换周期 42 天,较 Q3 的 33 天拉长,但好于上年的 49 天。库存周转环比变慢、应付提供的时间缩短,解释了为何高增长仍消耗现金。应使用同口径趋势,而不是以净收入分母推断这些周转指标。Q4 营运资本说明

Q3 正式分部表还可计算内存 GAAP 毛利率约 30.0%:275.067 百万收入减 192.565 百万成本。这个历史观测有用,但 Q4 与 FY2027 产品、价格和代理业务结构可能变化,本文不据此固定未来利润率。分部财务表

现金充裕与现金创造能力分开看 ​

年末现金等价物 647.2 百万,债务账面 789.0 百万,账面净债务 141.7 百万;另有 temporary equity 202.7 百万。现金较年初增加 193.5 百万,来自 CFO −151.9 百万、投资现金流 +58.1 百万和融资现金流 +287.3 百万相加。

融资流入中既有 750 百万新债,也有 93 百万受限客户预付款。预付款对应采购义务,其利用和负债对应关系仍需完整附注核对;这里不把整笔金额再从期末现金机械扣一次。年末流动债务账面 53.4 百万、现金余额较大,当前证据支持“融资缓冲尚在”,但不支持用现金余额掩盖经营资金需求。年末资产负债与现金流

FY2027 需要怎样的现金改善 ​

S&P Global 经 StockAnalysis 展示的 FY2027 FCF 聚合预测为 115.73 百万,即市场预期本身已经包含从负转正;它并非尚未被讨论的隐藏利好。相对后文条件式普通股市值,这只有约 2.41% 收益率。若再示意扣除 FY2026 优先股现金股息 12.233 百万,降至约 2.15%;未来转换会改变股息和普通股分母。聚合预测

下面由本研究自行设定的现金敏感性说明资金回收的价值。营运资本变化前现金 300–380 百万是假设范围,以 FY2026 的 222.1 百万为参照、假设 FY2027 盈利增长,尚未建立逐科目预测。它与股价情景独立,不冒充公司现金指引。

假设(百万美元)持续吸收资金部分恢复回收较强
营运资本变化前现金300340380
营运资本现金影响−250−170−70
资本开支与定金−15−20−25
FCF35150285
示意优先股现金股息−12−12−12
扣该股息后现金23138273

上表三档均假设盈利现金增长及资金占用改善,因此另做延续压力检查:若营运资本变化前现金仍为 FY2026 的 222.122 百万、资金占用仍为 374.036 百万,只将资本开支设为 15 百万、优先股现金股息设为 12 百万,则 FCF 为 −166.914 百万,扣该股息后为 −178.914 百万。这是沿用历史资金吸收程度的敏感性,说明现金继续为负仍是需要承受的路径。

每改善 100 百万营运资本占用,就增加约 100 百万现金,其影响远大于小幅节省资本开支。以上扣股息后金额仍未分配非控股权益、SBC 的经济成本或未来偿债资金,不能全部视为普通股可分配现金。在盈利增长的同时,若库存和应收继续按更快速度扩张,公司仍可能依赖融资。

稀释:已经发生的股份、潜在转换与现金义务 ​

先把不同分母放回对应时点 ​

股数口径(百万股)数量应如何使用
2026-07-02 普通股实际数51.243Q3 10-Q 封面;早于 7 月换股。
旧债交换发行股份约 8.7两批约 4.7 与 4.0;Q4 电话会确认整体交换完成。
仅将以上相加的条件式股数59.943尚缺员工发行、代扣、回购与其他结算;仅作估值桥的敏感性代理。
Q4 基本加权平均55.645一季加权数,换股只计入部分期间。
Q4 GAAP/调整后稀释加权64.040/62.000GAAP 与公司调整后对冲处理不同。
FY2027 指引稀释加权63.000与指引 EPS 4.45 配套;不能当作当前精确流通股数。

来源:Q3 10-Q、7 月融资 8-K、Q4 股数与指引。Yahoo 仍显示约 51.24 百万股,本文不采用该旧分母作为现时市值。

已经换出的约 8.7 百万股,构成以后每股盈利的基线;在采用 63 百万指引股数和对应 EPS 时,再加一次会重复计算。63 百万的逐证券组成尚未披露,不能精确拆成当前普通股、优先股、未归属奖励和对冲调整。

为了单独看股数风险,固定指引的盈利池为 4.45×63=280.35 百万,分母若从 63 增至 65/67 百万,则 EPS 分别降至 4.31/4.18,降幅约 3.1%/6.0%。这只是保持利润不变的机械敏感性;若优先股转换,同时会改变收益分配,真实 EPS 还需改分子。

750 百万新可转债主要改变长期现金义务 ​

新债票息为 0%,2031 年 8 月到期,初始转换价约 116.70 美元。Indenture 的结算方式要求现金偿还本金;默认组合结算时,每 1,000 美元本金的现金部分不得低于 1,000 美元,超本金价值才可能以股份支付。因此 6.427 百万初始 underlying shares 是合同计算基数,不能整笔作为即时增发量。2031 债券合同

公司支付 49.125 百万购买 capped calls,初始封顶价 175.05 美元。该对冲可抵消一定范围内的超本金价值,但只覆盖这笔新债,且受合同、特殊事件和对手方履约影响。融资公告、对冲合同格式

假设结算股价超本金股份等价(百万)capped call 抵消等价(百万)简化净剩余(百万)
80.16 美元0.0000.0000.000
150.00 美元1.4271.4270.000
175.05 美元2.1422.1420.000
200.00 美元2.6771.8750.802
250.00 美元3.4271.5001.927

这是所有新债按默认组合方式结算、观察期股价恒定、普通对冲匹配履约的简化演示;低于转换价的行表示没有超本金价值,并不表示当时必然发生转换。若整笔转换或到期,750 百万本金仍需现金偿付。实际观察期、提前转换、make-whole 与调整条款会改变结果。零票息缓解当期利息,未消除本金资金需求。

优先股与 SBC 仍有经济成本 ​

优先股原投入 200 百万,累积年股息 6%,按初始转换价 32.80784 美元计算,纯本金对应约 6.096 百万股,实际还受应计股息与调整影响。公司强制转换权要到 2026-12-13 之后才可能生效,并需满足连续交易日价格及其他条件。以初始价计算的 150% 门槛是 49.21 美元,现价高于该数值仍不代表转换已经发生。优先股合同

估值有两种一致口径:保留优先股索取权,或假设转换、移除该索取权并增加普通股。本文条件式 EV 使用前者;每股估值直接使用公司或分析师已给出的调整后稀释 EPS,未再扣一遍优先股价值。

FY2026 SBC 为 31.815 百万,调整后利润将其剔除,但股东仍需承受股份或回购现金成本。优先股参与收益分配也影响普通股可得利润:FY2026 归属公司净利润 180.615 百万,扣优先股股息 12.133 百万及参与分配 17.394 百万后,普通股净利润为 151.088 百万。评价市盈率时必须使用对应普通股 EPS。EPS 调节与分配

好价格:目前已经计入什么 ​

当前估值锚点 ​

统一采用 2026-10-13 09:54:49 EDT 的 80.1624 美元。Yahoo 同时给出“实时”来源名和 15 分钟延迟字段,本文保留时间戳并按指示报价使用。Yahoo 行情

指标当前计算分母与边界
条件式普通股市值4,805.2 百万美元股价×59.943 百万条件式普通股代理;精确现时市值未知。
指引盈利分母对应权益规模5,050.2 百万美元股价×63 百万加权稀释股数;仅解释每股口径,不当作精确普通股市值。
条件式账面 EV5,166.0 百万美元4,805.2+债务789.0−现金647.2+优先权益202.7+非控股权益16.4;未含租赁,使用账面值。
FY2027 指引调整后 P/E18.01 倍EPS 4.45;指引范围 3.75–5.15 对应 21.38–15.57 倍。
FY2027 共识调整后 P/E17.58 倍聚合 EPS 4.56。
FY2028 共识调整后 P/E15.24 倍聚合 EPS 5.26。
FY2027 聚合 FCF/条件式市值2.41%FCF 115.73 百万;未扣优先股现金股息。

价格为同一时点;公司财务和股数代理各自日期如上。EV 只用于展示索取权桥,估值结论主要依赖每股盈利情景,避免缺失精确股数造成虚假精度。

公司 FY2027 指引为收入约 2,430 百万、调整后 EPS 4.45。10 月 9 日更新的 S&P Global 聚合预期为 FY2027 收入 2,450 百万/EPS 4.56,FY2028 收入 2,750 百万/EPS 5.26。现有预期已经包含强劲增长,下一段上行需要超预期或更高估值。公司指引、聚合预期

Proactive 转述 BofA 的 100 美元目标,相对现价为 +19.84 美元、+24.7%,依据是约 18 倍 FY2028 EPS 5.43;18×5.43 实际为 97.74 美元,对应 +17.58 美元、+21.9%,目标带有取整。原券商模型和逐项调整尚未取得。该目标可以代表一条乐观路径,不能独立证明现价便宜。BofA 观点报道

三种价格情景 ​

以下使用 FY2028 调整后稀释 EPS × 估值倍数,考察未来 3–12 个月市场可能给予的远期估值。EPS 4.00/6.00、14/17/20 倍以及概率全部为本研究假设;基准 EPS 5.26 取聚合预期。倍数用于测试周期、现金质量和产品升级的定价差异,没有冒充同行估值中位数。

情景EPSP/E隐含股价较 80.1624 美元差额价格回报主观权重
下行情景4.0014 倍56.00−24.16−30.1%30%
基准情景5.2617 倍89.42+9.26+11.5%50%
上行情景6.0020 倍120.00+39.84+49.7%20%

下行情景对应内存价格/订单承压、资金占用未改善,盈利与倍数同时回落;56 美元只是本次选定的下行情景,并非最大损失边界。基准情景对应现有盈利预期兑现、现金逐步修复;上行情景要求盈利高于共识约 14%,并因产品/现金质量获得 20 倍估值。120 美元路径仍低于新 capped call 封顶价,但其他股份和优先股经济成本继续存在。

按上述主观权重,情景加权价值为 85.51 美元,相对当前 +5.35 美元、+6.7%。这是一种风险回报组织方法,不是精确目标价或已校准概率。若只把上行情景权重提高 10 个百分点、下行情景相应降低,加权价值就升至 91.91 美元,对应 +11.75 美元、+14.7%,显示结论对乐观概率相当敏感。当前证据不足以支持主动提高该权重。

以本研究对这类波动与现金不确定性要求的 15% 价格回报门槛衡量,85.51/1.15 约为 74.36 美元。进一步采用 70–72 美元作为重新考虑的价格区间,给未知股数和现金预测留余量;这是估值条件,不是图表支撑位。若盈利或订单同步恶化,价格到达该区间也不会自动产生吸引力。

现价仍有上涨路径,但“存在上涨空间”与“值得投入新增资金”之间,还有概率与下行损失这一步。 在当前假设下,基准 +11.5% 对应下行 −30.1%,加权 +6.7% 未达到所需补偿,所以本期选择 pass。

催化剂、反证与监控雷达 ​

指标或事件下一次查看什么对判断的影响
FY2026 10-K;提交后核对期末净普通股、旧债残余及换股结果、旧对冲处置、优先股、客户预付款与税项附注。股数显著高于当前假设会压低每股价值;会计桥完整能提高估值置信度。
下一次季度业绩及以后每季内存量价桥、backlog 金额/取消和收入确认、FY2027 EPS 指引。上修指引且来自交付/产品组合而非单次税项,支持提高盈利假设。
CFO、库存、应收及应付两季累计 CFO 是否转正;gross 口径 CCC 能否缩短,现金改善是否只是延后付款。收入继续增长而现金回收转好,支持提高估值质量;反向变化降低参与意愿。
CXL / MemoryAI 商业披露独立产品收入、毛利、付费部署、复购或客户署名的生产收益。出现可量化经济贡献才考虑增加该业务价值;单一演示或客户名称不足。
2026-12-13 后优先股条款是否满足并实际执行转换,股息与股份如何对应变化。需要同步重做盈利分子与分母;日期到达本身不构成转换事实。
DRAM 价格与供应,每季度复查价格增速放缓时,公司出货、毛利额及库存跌价是否稳定。定价回落伴随数量扩张是韧性证据;量价同降且库存高会加强下行情景。

最有力的多头反证,是公司以持续盈利增长证明资金占用可控,并把 CXL 变成可单列的利润来源。最有力的空头证据,则是高价库存尚未消化时订单延后、毛利承压且需要再次融资。下一阶段研究应围绕这些可观测差异更新,而不是围绕目标价数量更新。

快讯、文章与研报页内阅读 ​

文章与研报 ​

来源发布日期站内阅读状态
Stock Analysis management transcript2026-10-06Penguin Solutions业绩与AI订单加速原文与中文摘要已归档
Proactive via Yahoo Finance2026-10-09美国银行看好企鹅AI内存增长原文与中文摘要已归档
Penguin Solutions IR2026-10-06PENG FY2026 全年及第四季度业绩公告正文未取得:页面未返回可用正文。
TrendForce2026-09-30四季度存储价格与供需分化原文与中文摘要已归档
Penguin Solutions / Business Wire2026-10-12PENG 披露新 CFO 诱导性股权奖励正文未取得:来源页面拒绝访问,未取得正文。
StockAnalysis; transcript supplied by Quartr2026-07-07PENG第三季度业绩与AI业务展望原文与中文摘要已归档
Penguin Solutions2026-03-16PENG 发布 CXL KV cache 服务器正文未取得:页面未返回可用正文。
Astera Labs2026-03CXL扩展内存支持分布式推理原文与中文摘要已归档
打开原文

Penguin Solutions业绩与AI订单加速

重要性5/5 关键业绩与指引

公司原始业绩电话会同时给出实际业绩、全年展望、融资与营运资金信息,且 AI 基建和内存是 PENG 当前增长的主要业务。

中文摘要

核心结论

Penguin Solutions(NASDAQ: PENG)称,FY2026下半年 AI 基建和 Integrated Memory 需求提速,推动第四季度收入与利润创纪录;公司据此提高 FY2027 收入和 EPS 指引。管理层将增长归因于数据中心 AI 需求、内存产品及全栈 AI 工厂交付能力,同时披露营运资金占用增加、内存价格与供货、客户部署节奏等变量仍会影响收入构成和季度兑现。

重要性评级

评级 5/5;标签:业绩、指引、AI 基建、内存、融资。依据:公司管理层完整业绩电话会记录,包含 FY2026 实际业绩、FY2027 定量展望、业务分部表现、资产负债及问答中的执行限制,且与 PENG 直接相关。

关键事实

  • FY2026 Q4 净销售额 5.67 亿美元,同比增 68%;非 GAAP 摊薄 EPS 为 1.00 美元,同比增 133%。毛利率 28.8%,营业利润 9,000 万美元、营业利润率 15.8%,调整后 EBITDA 9,300 万美元。全年净销售额约 17.3 亿美元、同比增 26%;摊薄 EPS 2.87 美元、增 51%;营业利润 2.41 亿美元、增 44%,营业利润率 13.9%。管理层说明电话会中未另行标注的财务指标为非 GAAP,须结合公司发布的 GAAP 调节表阅读。
  • Q4 非超大规模 AI 基建与 Integrated Memory 合计占公司收入 78%,同比增 141%。Advanced Computing 收入 1.54 亿美元、增 11%;其中非超大规模 AI 基建收入增 99%,占该分部 66%。Integrated Memory 收入创纪录,达 3.41 亿美元、增 158%,约占公司收入 60%;FY2026 全年收入 9.24 亿美元、增 99%。Optimized LED Q4 收入 7,200 万美元、增 7%,全年 2.49 亿美元、降 3%。
  • FY2027 公司收入指引中点约 24.3 亿美元,约增 40%,区间为中点上下 10 个百分点;管理层另说明,以此前 FY2026 指引中点为基准计算,当前指引中点对应约 45% 增长。分部指引为 Advanced Computing 增约 40%(±10 个百分点)、Integrated Memory 增约 50%(±10 个百分点)、Optimized LED 大致持平。预计全年毛利率约 28%(±2 个百分点)、运营费用约 2.75 亿美元(±1,000 万美元)、摊薄 EPS 约 4.45 美元(±0.70 美元),对应 EPS 中点约增 55%;假设摊薄股数约 6,300 万股、有效税率约 20%。
  • 公司称 FY2026 结束时总订单积压创纪录;内存积压延伸至少四个季度,且仍在增长,但没有披露积压金额、其中覆盖 FY2027 收入的比例或逐个项目的收入确认时间。FY2026 Q4 内存收入同时受价格和出货量推动。公司称全年新增 17 个 AI 基建客户、12 个客户扩大业务;Integrated Memory 新增 50 个客户、29 个客户扩大合作。
  • AI 基建案例包括为韩国科技公司支持的 neocloud 设计、建设和管理 NVIDIA GB300 NVL72 平台;为一家有超过 30 亿美元多年期合同的上市 neocloud 提供多年部署和全天候运营服务;为另一家拥有来自领先 AI 实验室 100 亿美元算力合同的 neocloud 在挪威部署及运营 36,000 GPU AI 工厂。公司还提到一家 neocloud 客户的分布式微型数据中心项目,以及向 AI 服务器厂商供应内存、向推理服务商扩展 CXL 内存扩展卡合作。客户名称、金额合同属性和项目描述为公司在电话会上的陈述,记录未提供独立合同文件。
  • 公司将全栈平台描述为 ClusterWareAI 软件、MemoryAI/Integrated Memory、GPU/CPU 系统、OriginAI 架构及设计、建设、部署和托管服务。管理层称,推理和 agentic AI 增加上下文内存需求,CXL 扩展卡和 MemoryAI KV cache 面向相关场景;光子高带宽内存仍在路线图开发,CXL AIC 产品计划利用已安装 DDR4。公司也称已获 NVIDIA AI Factory Specialized Partner 认定,并新增 AI 硬件和内存供应安排。以上包含公司对产品定位、需求持续性和竞争优势的判断。
  • FY2026 Q4 末应收账款 7.96 亿美元、存货 7.49 亿美元、应付账款 8.17 亿美元,均较上年同期显著增长;现金转换周期 42 天,环比增加 9 天。Q4 经营活动现金流出 1.63 亿美元,全年流出 1.52 亿美元,管理层主要归因于支持内存和 AI 基建增长的营运资金投入。期末现金及现金等价物 6.47 亿美元。
  • 公司完成 7.5 亿美元、票息 0%、2031 年到期的可转债发行,初始转股价约 116.70 美元;配套 capped call 初始上限价约 175.05 美元。公司还将约 2.96 亿美元 2029/2030 年到期旧可转债本金换成现金与普通股,并偿还信贷额度下 1 亿美元余额。管理层称此举延长期限并提升流动性灵活度;发行、换债和 capped call 的最终稀释效果取决于转换及相关条款执行。

作者观点与证据

这是公司管理层与财务负责人在 FY2026 Q4 业绩电话会上的陈述,不是独立研究结论。收入、分部数据、费用、现金流和融资条款属于公司披露;对 agentic AI 带动内存需求、内存数据中心需求较具持续性、全栈平台形成竞争优势及增长机会可延续的表述属于管理层判断。分析师提问涉及 neocloud 项目经济性、利润率和积压覆盖,管理层称单位经济性与企业自建项目相近,但未披露分部利润率及积压覆盖收入的具体比例。

与相关标的的关系

直接涉及 PENG 的 FY2026 经营结果、FY2027 指引、产品路线、客户项目、营运资金和资本结构。NVIDIA、AI 服务器厂商、neocloud 与内存供应商作为合作生态或客户类别出现;电话会未给出多数客户名称及可核验合同细节。

时效性与限制

电话会日期为 10/06(未给出具体时刻)。FY2027 展望是管理层当前预期,明确受客户部署日程、组件供应与交期、内存价格和成本及产品组合影响;公司也指出项目时间可能造成季度收入波动。内存市场周期性、AI 需求持续性与竞争优势均有判断成分。部分项目金额是客户合同/算力合同的公司描述,不等同于 PENG 已确认收入或其订单金额。

后续跟踪

可对照后续披露观察 FY2027 收入及 EPS 与指引区间的兑现、Advanced Computing 和 Integrated Memory 的增长与毛利组合、内存价格/成本、积压转收入速度、客户部署和零部件供货节奏、应收及存货变化与经营现金流,以及可转债转换和稀释情况。

原文
Penguin Solutions (PENG) Q4 2026 Earnings Call Transcript & Audio

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Penguin Solutions, Inc. (PENG)

NASDAQ: PENG · Real-Time Price · USD

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79.26

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Oct 13, 2026, 9:48 AM EDT - Market open

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Earnings Call: Q4 2026

Oct 6, 2026

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  • Earnings release

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Summary

Q4 sales rose 68% YoY, with AI-driven businesses growing 141%; FY26 delivered record operating income and adjusted EBITDA. FY27 sales and EPS outlook increased on strong backlog, memory demand, and AI infrastructure momentum.

Operator

I will now hand the conference over to Lana Adair, investor relations. Please go ahead.

Lana Adair

Investor Relations, Penguin Solutions

Thanks everyone for joining us. With me today are Kash Shaikh, Stephen Cumming, and Aaron Johnson. Our earnings materials are available on the investor relations section of our website, and I encourage you to review these materials. Also, please take some time to review the presentation, which includes additional content to complement our discussion today. During this call, unless otherwise indicated, all references to financial measures refer to non-GAAP financial measures. Non-GAAP measures should not be considered in isolation from, as a substitute for, or superior to our GAAP results. A reconciliation of these measures to their most directly comparable GAAP measures can be found in our press release and accompanying slide presentation.

Statements made during this call that relate to future results and events are forward-looking statements based on current beliefs and assumptions and are not guarantees of future performance. Actual results and events could differ materially from those projected due to a number of risks and uncertainties, which are discussed in our press release, our earnings call presentation, and our SEC filings. Except as required by applicable law, we assume no obligation to update our forward-looking statements. Now I'll turn it over to Kash.

Kash Shaikh

President and CEO, Penguin Solutions

Good afternoon, everyone, and thank you for joining Penguin Solutions' Fiscal 2026 fourth quarter and year-end earnings call. Before we get into our results, I want to start with an important addition to our leadership team. Earlier today, we announced that Stephen Cumming has joined Penguin Solutions as our new Chief Financial Officer. Stephen is an experienced public company executive with a strong track record in financial leadership, operational scaling, and investor relations. We are pleased to welcome Stephen at an exciting time for Penguin, and investors will have the opportunity to get to know him in the coming months.

For today's call, Aaron Johnson, who has served as our interim CFO since July, will walk you through our financial results and fiscal 2027 outlook as planned. I want to thank Aaron for his leadership during the transition. I look forward to continuing to work with him as he returns to his role as Vice President of Finance and Accounting.

Now let's turn to our results. Our team delivered an exceptional quarter, capping off a very strong fiscal year. In Q4, we set company records for net sales, gross profit dollars, operating income, operating margin, net income, and adjusted EBITDA. Net sales were $567 million, up 68% year-over-year, and EPS was $1, up 133%, reflecting strong operating leverage that enabled EPS to grow faster than net sales. I would also like to thank our customers for their continued confidence in Penguin. Our AI-driven businesses continued to scale. In Q4, non-hyperscale AI infrastructure and Integrated Memory represented 78% of company net sales and grew 141% year-over-year.

Our overall company performance accelerated meaningfully in the second half of fiscal 2026, following the launch of our AI factory platform strategy and increased focus on the data center market. We aligned the business more closely with strong AI-driven demand, increased investments in product innovation, and accelerated go-to-market execution with a focus on neocloud and enterprise customers. The proof is in the results. After relatively flat year-over-year net sales in the first half, growth accelerated to 48% in Q3 and 68% in Q4, driving second half growth of 58%. Full-year net sales were approximately $1.73 billion, up approximately 26% year-over-year. We also achieved record full-year operating income. In AI infrastructure and Integrated Memory, bookings grew faster than net sales in the fourth quarter, and we ended fiscal 2026 with record company backlog and strong momentum heading into fiscal 2027.

Based on our second half performance, bookings growth, continued strength of our memory business, and more importantly, accelerating demand from neocloud customers for our data center AI infrastructure solutions, we are increasing our fiscal 2027 growth outlook from the preliminary view we shared last quarter. We expect record fiscal 2027 net sales with EPS growth expected to outpace net sales growth as we continue to scale and drive operating leverage.

I'll walk through what we are seeing in the market before turning to the outlook. AI is becoming super intelligence, SI, as it continues to move from chatbot advisor to agentic operator, executing tasks and workflows with increasing autonomy. This shift is driving inference at scale and creating increasingly memory-intensive workloads. Inference is increasing demand across the entire data center stack. GPUs remain critical, but CPUs and conventional memory, including DRAM, must also scale. We believe Penguin is well positioned to lead in this market opportunity at the intersection of data center AI infrastructure and memory solutions. We are seeing enterprise and AI native companies address these AI compute infrastructure needs in a variety of ways, primarily consuming AI infrastructure from neocloud providers, and in some cases, building their own on-premises AI factories.

For persistent high-utilization AI workloads such as inference, large enterprises are increasingly considering on-premises AI infrastructure, which can offer better token economics, data control, and performance. Our full stack AI factory platform combines five core elements with our partner ecosystem. First, ClusterWareAI operating system software for AI factories. Second, MemoryAI and Integrated Memory solutions. Third, GPU and CPU systems under our ComputeAI brand. Fourth, OriginAI factory architectures, and fifth, end-to-end design, build, deploy, and managed services.

What sets our platform apart is its rare combination of OEM-like differentiated product innovations and end-to-end AI infrastructure systems integration capabilities. This allows us to serve as the single builder and operator of AI factories across the entire life cycle for our customers. Customers need more than hardware and assembly. This is particularly important for neocloud providers, where we have seen a recent surge in interest that we believe reflects strong product-market fit for our full stack platform. For neoclouds, we can serve as a single operating partner across the entire life cycle, helping customers meet service level agreements and generate revenue faster while they focus on securing additional off-takers and scaling their businesses.

Let me now turn to our business results, starting with Advanced Computing. In Q4, Advanced Computing net sales totaled $154 million, representing 27% of company net sales and growing 11% year-over-year. Data center AI infrastructure growth within Advanced Computing, driven by our AI factory platform, was significantly stronger. Non-hyperscale AI infrastructure net sales grew 99% year-over-year and represented 66% of Advanced Computing net sales, up from 60% in the prior year quarter. This growth more than offset continued runoff from other traditional businesses and reflects the ongoing shift in Advanced Computing toward data center AI infrastructure.

During the quarter, we added six new AI infrastructure customers, including four neoclouds, a large quantitative trading firm, and an enterprise customer. We exited fiscal 2026 with strong momentum, and bookings have accelerated further entering fiscal 2027. Our land and expand model remains central to our go-to-market strategy. Successful initial deployments create opportunities to expand across our platform, including ClusterWareAI and multi-year managed services engagements that typically span three to five years.

Across fiscal 2026, we added 17 new AI infrastructure customers, while 12 customers expanded their business with Penguin. We are seeing strong momentum with neocloud customers, including a neocloud backed by a leading South Korean technology company selected us to design, build, deploy, and manage an NVIDIA GB300 NVL72-based platform with the initial deployment expected to support a broader GPU-as-a-service expansion. A publicly traded neocloud provider that has more than $3 billion in signed multi-year AI infrastructure contracts selected Penguin for a multi-year engagement to provide AI infrastructure deployment and 24/7 operation services supported by ClusterWareAI, our AI factory operating system software. Lektra, a neocloud, selected our full stack AI factory platform to deploy and optimize distributed AI micro data centers powered by existing carbon-free energy. We provide validated reference designs, NVIDIA-based AI GPUs and expert services with ClusterWareAI and support.

For another neocloud provider with $10 billion in contracted compute from a leading AI lab, we were selected to deploy and operate a 36,000 GPU AI factory in Norway. This multi-year engagement demonstrates the scale of our platform and our ability to bring large-scale AI infrastructure into production. These engagements demonstrate an important evolution in our business. Customers are increasingly selecting Penguin as an end-to-end partner to design, deploy, optimize, and operate AI infrastructure. We also continued to advance ClusterWareAI with new agentic AI capabilities that automate AI factory operations, remediation, and infrastructure monitoring. Our AI Factory Operations Agent provides administrators with a natural language interface for operational insights. Put simply, administrators can now talk to their AI factory.

We are also an NVIDIA AI Factory Specialized Partner, an invitation-only designation that reflects the depth of our partnership with NVIDIA, which continues to strengthen and more than a decade of experience building and managing NVIDIA-based AI factories. Finally, we established a relationship with another AI hardware supplier to help expand supply availability and support growing demand. Now turning to Integrated Memory. Fourth quarter net sales reached a record $341 million, representing 60% of company net sales, up 158% year-over-year and 24% sequentially. In Q4 fiscal 2026, we added two new memory customer logos. Our land and expand strategy also continues to produce results. Across fiscal 2026, we added 50 new customers, while 29 existing customers expanded their business with us. We are seeing an increase in both volume and pricing.

Our growth reflects strong data center demand driven by agentic AI, including increasing memory requirements from AI and inference workloads. While memory is a cyclical market, we believe AI is adding a structural demand driver, particularly for memory solutions in the data center. Penguin operates differently from a traditional commodity memory manufacturer. Our focus is primarily on data center solutions. Our engineering design, firmware development, advanced validation, negotiated pricing, and asset-light model are designed to deliver more resilient economics across memory cycles. We continue to see strengthening bookings and pipeline for our CXL memory expansion products. A couple of proof points. In Q4, we secured business with a leading global AI server manufacturer whose platforms support large-scale AI training and inference. This win reinforces the strength of our memory portfolio for data center AI infrastructure.

We also expanded our relationship with a leading next-generation AI infrastructure inference provider, rapidly scaling AI inference deployments with our CXL memory expansion cards. Beyond our core memory business, we are developing new solutions designed to address the growing memory bottleneck in data center AI infrastructure. Our CXL-based large memory expansion cards and MemoryAI KV cache solution are designed to increase memory capacity and improve the economics of agentic AI and real-time inference. Our MemoryAI roadmap also extends to future memory appliances, including photonic high-bandwidth memory solutions designed to expand GPU memory capacity and bandwidth. We are also developing a CXL AIC product line designed to repurpose installed DDR4 memory for AI infrastructure, helping customers extend asset life and lower the cost of scaling memory-intensive workloads.

Finally, we established a new supply arrangement with a leading memory supplier to support the growth of our data center AI infrastructure portfolio and strengthen our supply position. Together, these developments reinforce our long-term focus on data center memory and AI infrastructure. Turning briefly to Optimized LED. Our LED business also performed well in the quarter, with Q4 net sales totaling $72 million, up 7% year-over-year. Our strategic investment priorities remain focused on data center, AI infrastructure, and memory. While we continue to manage our LED business with discipline. As we enter fiscal 2027, our focus is not simply on capturing demand. It is on building a more repeatable and scalable operating model around that demand while keeping customer success at the center of everything we do. As part of that, we are deploying agentic AI across our operations with a focus on measurable business outcomes.

Our engineering teams are using agentic AI coding workflows to accelerate ClusterWareAI development. While AI-driven tools are helping compress product cycles and improve supply chain responsiveness. With that, let me share our updated fiscal 2027 outlook. During our Q3 earnings call, we provided preliminary fiscal 2027 expectations for net sales and diluted EPS growth of approximately 30% year-over-year from the midpoint of our fiscal 2026 outlook. Based on our fourth quarter results and AI-driven demand, we are providing a fiscal 2027 outlook that exceeds that preliminary view for both net sales and diluted EPS. We expect our diluted EPS to grow faster than net sales due to our strong operating leverage. Aaron will provide the details in a moment.

Let me close with three observations. First, our strategy is working and growth is broadening. Our AI-driven businesses represented 78% of Q4 net sales and grew 141% year-over-year, with Integrated Memory remaining strong and AI infrastructure now accelerating significantly. Second, strong operating leverage is driving profitable growth while enabling continued investment in innovation. EPS is growing faster than net sales while we continue investing in differentiated products and capabilities. Third, we believe the value creation opportunity is durable and still in the early innings with our position at the intersection of AI infrastructure and memory combined with ClusterWareAI and services, creating a differentiated long-term advantage.

I want to thank our team for their focus and execution throughout fiscal 2026 and for the strong momentum we carry into fiscal 2027. With that, I'll turn it over to Aaron to walk through the financials and our outlook.

Aaron Johnson

VP of Finance and Accounting, Penguin Solutions

Thank you, Kash. I'll begin with our fourth quarter and full year fiscal 2026 results and then turn to our outlook for fiscal 2027. In the fourth quarter, net sales and earnings per share exceeded our outlook, reflecting growing adoption of our AI infrastructure solutions and continued AI-driven demand for data center memory. Strong net sales growth, disciplined expense management, and improved profitability drove meaningful operating leverage. We set company records for net sales, gross profit dollars, operating income, operating margin, net income, and adjusted EBITDA. For the full year, we also set records for gross profit dollars and operating income. Starting with the fourth quarter, net sales were $567 million, up 68% year-over-year and up 18% sequentially. Gross margin was 28.8%, operating margin was 15.8%, and diluted earnings per share was $1, up 133% year-over-year.

For the full year, net sales were $1.73 billion, up 26% versus fiscal 2025, while diluted earnings per share increased 51% to $2.87, roughly twice the rate of net sales growth, demonstrating the operating leverage in our model. Fourth quarter product net sales were $510 million, or 90% of total company net sales, up 86% year-over-year. Services net sales were $57 million, or 10% of total net sales, down 11% year-over-year, reflecting lower hyperscale services volumes, partially offset by growth in multi-year AI factory managed services. Net sales by business segment were as follows. For Advanced Computing, fourth quarter net sales were $154 million, up 11% year-over-year and 12% sequentially. For the full year, Advanced Computing net sales were $559 million, down 14%, reflecting the wind down of Penguin Edge and lower hyperscale hardware sales.

Non-hyperscale AI infrastructure business grew 99% in the fourth quarter and represented 66% of segment net sales, while growing 72% for the year and reinforcing its position as the segment's primary growth driver. For Integrated Memory, fourth quarter record net sales were $341 million, representing 60% of total company net sales, up 158% year-over-year and 24% sequentially. For the full year, Integrated Memory net sales were $924 million, up 99% and represented 53% of total company net sales, up from 34% in fiscal 2025. The growth reflects strong demand for memory supporting AI-driven data center applications, consistent with our strategic focus on this market. For Optimized LED, fourth quarter net sales were $72 million, representing 13% of total company net sales, up 7% year-over-year. For the full year, net sales were $249 million, down 3%.

The business delivered improved profitability as we continued to execute with discipline and focus on profitable opportunities. Fourth quarter gross margin was 28.8%, up 70 basis points sequentially, supported by improved profitability in Integrated Memory, partially offset by lower Advanced Computing margins. Together with strong net sales growth, this drove record gross profit of $163 million, up 21% sequentially and 57% year-over-year. For the full year, gross profit reached a record $508 million. Gross margin was 29.3%, down 1.7 percentage points, primarily due to a greater contribution from AI infrastructure hardware, even with stronger margins in Integrated Memory. The growth in gross profit dollars demonstrates the leverage generated by our increased scale. Fourth quarter operating expenses were $73 million, up 4% sequentially and 13% year-over-year. The sequential increase primarily reflects higher incentive compensation associated with our strong financial results.

For the full year, operating expenses increased 4% to $267 million, well below our 26% growth in net sales. As a result, operating expenses declined by 3.4 percentage points as a percentage of net sales, reflecting a structural improvement in the scalability of our operating model. That combination of higher gross profit dollars and expense discipline drove record fourth quarter operating income of $90 million, up 39% sequentially and 129% year-over-year. With net sales growth significantly outpacing operating expense growth, operating margin expanded 2.4 percentage points sequentially and 4.2 percentage points year-over-year to 15.8%. For the full year, operating income reached a record $241 million, up 44%, with operating margin expanding 1.7 percentage points to 13.9%. These results demonstrate how scale and disciplined spending are translating growth into higher profitability. Fourth quarter diluted earnings per share was $1, up 19% sequentially and 133% year-over-year.

The sequential EPS growth rate trails our 39% operating income growth, reflecting a higher share count following our convertible notes offering. For the full fiscal year, diluted earnings per share were $2.87, up 51% and $0.22 above the high end of our July outlook. Adjusted EBITDA was a record $93 million in the fourth quarter, up 115% year-over-year and 38% sequentially. Full year adjusted EBITDA was $256 million, up 37%. Before I walk through working capital, a reminder that we calculate days sales outstanding, days payables outstanding, and inventory days on a gross sales and gross cost of goods sold basis. Gross sales were $1.57 billion, and gross cost of goods sold was $1.42 billion in the fourth quarter. The difference between gross and net sales relates primarily to our memory business' logistics services, which are accounted for on an agent basis.

Turning to working capital, net accounts receivable increased to $796 million from $308 million a year ago, reflecting the growth in our memory business and the associated gross billings. Despite the higher balance, days sales outstanding improved to 46 days from 53 days last quarter and 51 days a year ago. We are managing these investments against customer deployment schedules and supply availability as we support growth. Inventory increased to $749 million from $255 million a year ago, reflecting purchases to support record backlog and expected customer deployments across our memory and AI infrastructure businesses, as well as higher memory costs. Inventory days were 48, up from 42 last quarter as we positioned inventory against a record backlog, and down from 51 a year ago.

Accounts payable increased to $817 million from $267 million a year ago, reflecting purchasing activity to support the growth of our memory and AI infrastructure businesses, as well as higher memory costs. Days payable outstanding was 52, down from 62 last quarter and 54 a year ago, primarily reflecting the timing of purchases within the quarter. Our cash conversion cycle was 42 days, compared with 33 days last quarter and 49 days a year ago. Funding growth efficiently and maintaining disciplined working capital management remain important priorities. Moving to our broader capital structure, during the quarter, we completed a significantly oversubscribed $750 million 0% convertible note offering due in 2031, which we believe reflects strong investor confidence in our business and long-term strategy. The notes have an initial conversion price of approximately $116.70 per share, representing an approximately 50% premium to our share price at pricing.

We also entered into capped call transactions designed to reduce potential dilution up to an initial cap price of approximately $175.05 per share. Concurrently, we exchanged approximately $296 million principal amount of our 2029 and 2030 convertible notes for a combination of cash and shares of common stock and used a portion of the proceeds to fully repay the $100 million outstanding under our credit agreement. This disciplined and proactive approach extended our maturity profile and strengthened our capital structure, providing additional flexibility to invest behind our AI factory platform strategy and the growth opportunities ahead. Following these actions, we ended the fourth quarter with cash and cash equivalents of $647 million. That is up $207 million from the third quarter and $193 million from a year ago. We remain focused on managing liquidity and the working capital required to support our growth.

Cash used in operating activities was $163 million in the fourth quarter and $152 million for the full year, primarily reflecting working capital investments to support growth in our memory and AI infrastructure businesses. This compares with cash used of $70 million in the prior year quarter and cash provided of $113 million in fiscal 2025. We believe our capital structure and liquidity provide flexibility to fund this growth while improving working capital efficiency remains an important priority for fiscal 2027. Capital expenditures were $4 million in the quarter and $12 million for the year, well under 1% of net sales, with depreciation expense of $5 million and $20 million respectively. Our asset light model allows us to scale without significant capital spending, and we expect that to continue in fiscal 2027. Now turning to our outlook.

We enter fiscal 2027 with strong momentum supported by our second half performance, record backlog, bookings growth, continued Integrated Memory strength, and further acceleration in demand for our AI infrastructure business. We expect strong net sales growth to increase gross profit dollars while operating expenses grow at a considerably slower rate. That combination is expected to expand operating margin and drive diluted EPS growth that outpaces net sales growth. For fiscal year 2027, we expect net sales to grow approximately 40% at the midpoint, ± 10 percentage points, or an increase of approximately $700 million to approximately $2.43 billion at the midpoint. Last quarter, we shared a preliminary view of approximately 30% growth from the midpoint of our then current fiscal 2026 outlook. Using that same fiscal 2026 outlook midpoint as the starting point, our current fiscal 2027 midpoint represents growth of approximately 45%.

The range reflects different growth expectations across our businesses, as well as variability in customer deployment schedules and the availability, lead times, and cost of certain products and components. Our record backlog, together with the demand we are seeing across the business, provides a strong foundation for our fiscal 2027 growth expectations. At the same time, customer deployment schedules, product availability, and evolving memory market conditions could affect the mix and cadence of net sales throughout the year. We believe the range appropriately balances the opportunities ahead with these execution and market variables. Looking at the net sales outlook by segment. For Advanced Computing, we expect full year net sales to grow approximately 40% at the midpoint, ± 10 percentage points, driven by continued momentum in non-hyperscale AI infrastructure, partially offset by the absence of Edge sales and lower expected hyperscale sales.

For Integrated Memory, we expect full year net sales to grow approximately 50% at the midpoint, ± 10 percentage points, driven by continued strong demand for memory supporting AI-driven data center applications. For Optimized LED, we expect full year net sales to be relatively flat year-over-year as we continue to manage the business with discipline. We expect full year gross margin of approximately 28%, ±2 percentage points. Our outlook reflects the expected mix across our businesses, including the mix of hardware, software, and services, as well as our current assumptions for memory pricing and costs. While these factors influence the gross margin rate, we expect net sales growth and scale to drive higher gross profit dollars. We expect full year operating expenses of approximately $275 million, ± $10 million.

Within that range, we are reallocating resources toward our highest return AI infrastructure and memory programs while holding total spending growth well below net sales growth. This disciplined investment approach, together with higher gross profit dollars, is expected to drive further operating margin expansion. For the full year, we expect diluted earnings per share of approximately $4.45, ± $0.70, representing growth of approximately 55% at the midpoint and exceeding our expected rate of net sales growth. The EPS outlook reflects higher gross profit dollars, disciplined operating expense growth, and further operating margin expansion. The range also allows for variability in the timing of large customer deployments as individual project schedules can shift between quarters. Our EPS outlook assumes a diluted share count of approximately 63 million shares and an effective tax rate of approximately 20%.

We anticipate using this normalized rate throughout fiscal 2027, though it may change with our geographic earnings mix and developments in the global and U.S. tax environment. Our fiscal 2027 outlook is based on the current demand environment, backlog, customer deployment schedules, expected product mix, and our present assumptions regarding supply availability and costs. The timing of customer deployments and product availability may create variability between quarters, and certain components continue to have extended lead times that can affect project and shipment timing. Our outlook also incorporates current assumptions for memory supply and costs, which may evolve during the year, including the expected impact of new memory and AI infrastructure suppliers that strengthen our overall supply position and support growth. These factors may affect quarterly cadence and mix but are reflected in our full year planning assumptions.

Overall, our outlook reflects the momentum in our AI-driven businesses and the benefits of a more scalable operating model. We plan to invest selectively behind our highest return opportunities while maintaining expense discipline. Our objective is clear: convert strong net sales growth into higher gross profit dollars, operating margin expansion, and earnings growth that outpaces net sales growth. Please refer to the non-GAAP financial information section and the reconciliations of GAAP to non-GAAP measures in our earnings release and the investor materials available on our website for additional detail. With that, operator, we are ready for Q&A.

Operator

We will now begin the question and answer session. Please limit yourself to one question to ensure everyone in the queue has a chance to ask. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, press star one again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device. Please stand by while we compile the Q&A roster. Your first question comes from the line of Katherine Murphy with Goldman Sachs. Katherine, your line is now open.

Katherine Murphy

Analyst, Goldman Sachs

Great. Thank you for the question. Very helpful to see the improved Advanced Computing segment guidance for 40%, ±1 0% for fiscal 2027. I think last quarter you had talked about mid-teens growth as being the preliminary starting point for your outlook there. My first question would be, can you help us think about what has improved in the last 90 days that allows you to take up your outlook for Advanced Computing? And as a follow-up, as Penguin Solutions expands more into the neocloud customer type, how does that change the unit economics relative to some of the enterprise customers and quant wins that you had in the first part of 2026? Is there more hardware? How do we think about the margin impacts? Anything that you can share to help us from a modeling perspective would be helpful. Thank you very much.

Kash Shaikh

President and CEO, Penguin Solutions

Thanks, Kat, for the questions. To your first question about our initial preliminary guidance last quarter in the mid-teens over to 40% for Advanced Computing, mainly driven by AI infrastructure. This is primarily the growth that we have seen since the last earnings announcement. The booking strength, we have booked more than we expected, and we have very strong pipeline and, as you heard, some of these bookings that I shared as examples give us the confidence that our memory business strength continues, and our AI infrastructure is further accelerating, which is really resulting into the increased guidance for AI infrastructure, driving Advanced Computing guidance, and also increasing the guidance at the company level, primarily driven by AI infrastructure.

On your question about neoclouds and unit economics. Unit economics are quite similar to our enterprise on-premise AI factory deployments. However, one of the things we have observed with the neocloud customers, we have a pretty unique product market fit for these neocloud customers. As in, we can provide them end-to-end build and operate their factories on their behalf, including procurement of the hardware, end-to-end designing the hardware, building the data center, and then taking on the management of the data centers day two and beyond for up to three to five years, as I shared in some of the examples, along with our ClusterWare, which is really our differentiation in terms of how we deliver the services and, more importantly, meet the SLAs, which are really important for these neocloud customers to be able to provide SLAs to their off-takers.

All in all, similar unit economics, further expansion, and pretty unique product market fit with neocloud customers.

Operator

Your next question comes from the line of Sajal Dogra with Rosenblatt Securities. Sajal, your line is now open.

Sajal Dogra

Analyst, Rosenblatt Securities

Hey, thank you for taking my question and fantastic results. You are essentially front-running my bull case by a full year, so congrats. For my question, on your previous outlook, your revenue growth and earnings growth was roughly in line, but now you are finally showing operating leverage for next year. I just want to understand what has changed versus 90 days ago that has allowed you to drive that earnings expansion. I was wondering, how much of that is memory, or are we finally seeing some margin expansion in Advanced Computing as well? Thank you.

Kash Shaikh

President and CEO, Penguin Solutions

Sure. Sajal, first of all, the outlook we provided last quarter was more of a color and preliminary outlook than the actual outlook because it was sooner than we provide, right? As in typically we provide in Q4 earnings, the time frame where we are not providing the update, so that was much more directional, versus what we are providing is looking at all the aspects, and we have more visibility and more clarity. Also the confidence of operating in this new model, where if you look at Q3, our EPS grew faster than net sales company level, and then same in Q4, net sales grew 68% versus the EPS growth of 133%.

The line of sight we have in terms of the bookings and then the conversion ratios and so on and so forth, we have much more clarity, and it is much more of a formal outlook than the high-level color we provided directionally. As we are closer, we have more visibility, and we have more confidence in continuing the model as we scale the business. In terms of the margins at the segment level, we do not disclose the margins at the segment level. But I can tell you at the high level, memory continues to be strong, stronger than we expected, given the value we provide and the focus on the data center. Advanced Computing overall has the advantage of the services and the software.

However, as we increasingly have the wins where we have both hardware and the software, AI infrastructure may have some downward pressure. But at the same time, the advantage of ClusterWare software as well as the services has a very high attach rate in general with these opportunities that I shared in the earnings call earlier and the rest of the pipeline we see. So that is at the high level to give you an idea of how we are looking at the segment margins.

Operator

Your next question comes from the line of Ananda Baruah with Loop Capital. Ananda, your line is now open.

Ananda Baruah

Analyst, Loop Capital

Yeah. Thanks, guys, for taking the question. Really appreciate it. What are some of, just in keeping with Kash, Aaron, and Stephen, welcome, the dynamics that are sort of underpinning not just the increase in the fiscal year 2027 guide, but the acceleration of the growth rate actually across the key businesses? What are some of the mechanics that you are seeing out there in the marketplace that are leading to increased neocloud capture? What are you seeing to the degree that you are able to discern from commercial enterprise? I would love to just get a little context, if it is worth providing, on the degree to which the NVIDIA and Dell relationships are actually contributing to some of these dynamics at an increased pace. Thanks a lot. Appreciate it.

Kash Shaikh

President and CEO, Penguin Solutions

Sure, Ananda. Starting with the acceleration of the business, as I mentioned earlier, part of it is the increased visibility that we are seeing as well as the growth acceleration. We believe one of the competitive advantages we have is we are not a product company that is just providing the hardware and leaving at the doorstep of the customers. We actually work with them much earlier in the cycle, get involved in their design based on the requirements they have, whether they are a large enterprise customer deploying an on-premise factory or a neocloud provider, and then work with them consultatively understanding their requirement to, if it is a neocloud provider, what is their SLAs, who are their customers, and how can we align the design to meet their requirements?

That consultative aspect, and especially the fact that we have been doing it for more than two decades, that is an advantage. Our team was working with NVIDIA, designing these clusters for more than a decade. We have this advantage of understanding the architecture. The discussions are at the architecture level. We have the software, the ClusterWare that brings together all of these elements and makes it much more easier to build and manage these factories. Then end-to-end staying with them and helping them manage as a part of our managed services for them to get those outcomes when they are investing so much capital in these AI factories or data center is really a unique advantage as compared to the alternatives they have, whether they go to product-only companies or services companies.

There is not a unique combination that we provide with our platform between the products we have and the services that we provide. That is one of the advantages we are realizing is helping us. NVIDIA partnership is certainly helping us because we were recently nominated by NVIDIA as the AI Factory Specialized Partner. We are one of the few handful partners they have, and this is really the experience working with them and all of the things our team is capable of between design, build, deploy, and manage helps us have that credibility. Again, when we get involved in these designs and we are having the whiteboarding sessions with the customers to meet their requirement, that is a very unique competitive advantage we have along with our product. That is mainly for obviously AI infrastructure, which is accelerating.

In the memory business, one of the things we have done since we launched our AI factory platform strategy midway through the fiscal 2026, we have been focusing more on the data centers for two reasons. One is the fact that while memory demand is high everywhere in other segments, including the consumer segments, we believe that demand may still be just a demand and supply dynamics and may be cyclical. However, the demand in the data center for memory is driven by AI, especially with the agentic AI workloads, which are automating the workflows and creating really the productivity for the enterprises. That is going to sustain much longer. If anything, let's say agentic AI is adopted in the enterprises 10%, because they are still getting the workflows defined and the tools defined. For every 10% increase, that demand can double.

We continue to see that demand increasing for our memory products in the data center. So that positioning in the data center is another advantage, both in terms of increased demand as well as the durability of the demand for us. So combine that with these two tailwinds we have, memory business position in the data center, solving memory issues, providing the solutions for OEMs, and now increasingly providing forward-looking data center solutions such as CXL memory expansion cards. Those memory expansion cards are primarily for inference workloads, and we see this CXL business taking off very strongly. The pipeline is increasing, bookings are increasing. So those newer solutions, data center focus, inference focus, is another advantage for us as a differentiation and solving the inference challenges in the data center. So those are some of the things.

In addition to that, one more thing we are seeing is, in general, going back to AI infrastructure. Within the enterprises, they are primarily using neocloud as a service for their AI infrastructure. However, in some cases, they are considering on-premise, especially large enterprises, because of the fact that at scale, when they are using inference applications, cost becomes a challenge, especially with inference workloads. At that point, while they are getting the productivity, they are not seeing the ROI for the dollars they are investing, and they consider on-prem factories, and then we get involved, and they are looking to address their cost challenge. They are also considering the open weight models, because that's another way for them to make the economics better as they are moving from a cloud-based service to on-premise.

These are some of the trends where we have a very unique fit between our memory business as well as AI infrastructure business that is driving the growth for these two businesses.

Operator

Your next question comes from the line of Rustam Kanga with Citizens. Rustam, your line is now open.

Rustam Kanga

Analyst, Citizens

Great. Thank you for taking my questions, and congrats on a memorable close to the year with continued momentum in the AI infrastructure-driven business. Kash, you stated that the memory growth was a function of both price and volume. I believe last quarter you made the interesting point that if you were to count the backlog that you couldn't ship because of supply constraints, that that split would be closer to even or even lean more towards volume. I'm just curious, how much of that backlog has started to convert here? Then as you look ahead to next year, how are you thinking about that price versus volume mix in the memory business, especially as you focus on new customers, primarily with data center solution use cases? Thanks.

Kash Shaikh

President and CEO, Penguin Solutions

First of all, I want to give you the credit for remembering my comment. You are right, that was what we observed last quarter, where the revenue was a pretty strong record revenue for memory, even though the backlog was as much as our revenue. Fortunately, we are seeing the same trend continue. As much as we had another record quarter for memory, our backlog, if any, is much higher than the revenue that we recognized in this quarter. The backlog now extend to at least four quarters, and it continues to strengthen, which is why we have the confidence to increase the outlook for the business.

It is really, as I mentioned in the previous question, that function of data center focus demand for our core memory business, as well as the new CXL-based products that we are developing, had a very strong pipeline and demand because of inference taking off in the data centers.

Operator

Your next question comes from the line of Matthew Calitri with Needham & Company. Matthew, your line is now open.

Matthew Calitri

Analyst, Needham & Company

Hey, guys, Matt Calitri at Needham here. Thanks for taking the questions and great to see the strength to close the year here, including with some of those neocloud wins you talked about. I am wondering if you could give any more detail there on when will these projects start and begin to be recognized in the model and what the impact could be. Also, is there anything specific to point to as to why you had so much success closing deals in the quarter? Or just more broadly, why now is the moment for this neocloud opportunity?

Kash Shaikh

President and CEO, Penguin Solutions

Yeah. We are not sharing that level of details in terms of which project when, but I can tell you at the high level, most of these projects that I shared have already started. As in, we are managing their data centers or building their data centers. Some of them, we are in the process of procurement. They are not future, they are already happening. In fact, we were working on these projects for several months, even though some of it has accelerated into the bookings recently. We were working on these neocloud opportunities along with other enterprise opportunities, which is why I was referring to neocloud as a growth opportunity for us in the previous earnings announcement, even if we were not in a position to share the details.

Now that we have started working with them and we are managing these factories in some cases, and we are seeing increased bookings, that is why we are sharing more details. We will continue to share more details around these neocloud deployments. But as I mentioned, one of the reason being, the unique product market fit we have between the products we provide as well as our expertise in designing these AI factories or data centers, decade-long expertise to be able to help them achieve their goals, it is a pretty unique competitive advantage we have in the market with these opportunities.

Operator

Our next question comes from the line of Brian Chin with Stifel. Brian, your line is now open.

Brian Chin

Analyst, Stifel

Hi, good afternoon. Congratulations on the results, and thanks for letting us ask a question. Maybe a two-parter here. Given that record backlog commentary coming into this fiscal year and the growth guide for computing, roughly how much of your fiscal 2027 Advanced Computing revenue outlook is already in backlog? The other part of this question is, obviously really strong flow through in terms of the model and the non-GAAP EPS guide for next year. On that really low OpEx increase year- to- year, are you investing enough in terms of headcount retention areas to maybe improve bandwidth and utilization and go after more projects? With the revenue growth, it seems like you have a lot of coverage maybe to invest even more.

Kash Shaikh

President and CEO, Penguin Solutions

Right. Let me address your first question. We are not sharing the details of how much of our backlog is covering the revenue. However, I can tell you at the high level, it is pretty strong backlog across both memory as well as AI infrastructure. That is why we refer to as a record backlog. In terms of your question about operating expenses, when we say the strong operating leverage, it is really a function of the efficient operating model we have. When I say efficient operating model, to be specific, for example, for R&D, research and development in our engineering. Predominantly 95%+ of our engineering is in India, low-cost location, which gives us an advantage to scale much more cost effectively. We also have our memory engineering in Taiwan, another cost-effective location.

Even as we scale, our cost is very efficient as compared to other companies who may not have as much of a cost-efficient model. Our manufacturing is in Malaysia, all low-cost location here. Even as the business scale, that is a fixed cost for us as we add the capacity, and we have the capacity. Even if we add the capacity, the cost or the expenses do not increase linearly with the revenue. We are also using AI pretty extensively across the company. For almost eight or nine months, we have had a lot of productivity gains. As an example, 100% of our software engineers, wherever they are using code generation, so AI for code generation, which is helping us scale beyond what we can do in a traditional model.

We are using AI in pretty much every functions, including finance, legal, and that is helping us scale much more effectively. Between building this model and locations that are in cost-effective location and scaling in those areas, as well as using AI, it is a pretty efficient model. To answer your questions, for sure, we are investing in innovation, which is why we are winning more. The products I mentioned, ClusterWareAI is a differentiation helping us win more. CXL for the memory products is another advantage, and we will continue to invest more. While the dollars may not seem to increase, the productivity as well as the scale is increasing based on our efficient model driving the operating leverage, which allows us to continue to deliver more profitability.

If you look at our operating profit, it is a record of $90 million, was a record last quarter, almost $64 million. We continue to expect that while we will drive profitable growth, based on a scalable model, efficient model we have created, we can increasingly invest in innovation, and we will, even though the productivity will continue to increase for the company.

Operator

There will be no further questions at this time. This concludes today's call. Thank you for attending. You may now disconnect.

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美国银行看好企鹅AI内存增长

重要性3/5 分析师观点

首次覆盖评级和 FY2028 预测可补充市场预期,但内容为媒体转述单家投行观点,证据层级低于公司原始披露。

中文摘要

核心结论

Proactive 报道称,美国银行(BofA)首次覆盖 Penguin Solutions(NASDAQ: PENG),给予 Buy 评级,目标价 100 美元。该目标价按银行对 FY2028 每股收益 5.43 美元的预测给予 18 倍市盈率;银行预计 FY2028 收入 28.7 亿美元,高于当时分析师一致预期的 26.6 亿美元。

重要性评级

评级 3/5;标签:分析师覆盖、AI 内存、估值。依据:内容与 PENG 直接相关并包含可量化预测,但这是对单家投行首次覆盖观点的媒体转述,不是公司业绩披露或已实现结果。

关键事实

  • 报道称 BofA 对 PENG 首次覆盖并给出 Buy 评级、100 美元目标价。报道用约 18 倍该行预测的 FY2028 EPS 5.43 美元解释目标价;直接相乘为 97.74 美元,100 美元目标包含取整。
  • BofA 预测 FY2028 收入 28.7 亿美元、EPS 5.43 美元;报道列出的分析师一致预期分别为 26.6 亿美元和 5.04 美元。以上均为预测值。
  • 报道引用 PENG 最新季度 AI 驱动业务收入占比 78%、同比增长 141%,并称其 GPU/CPU 集群优化能力使公司对 neocloud 具有战略意义。
  • 报道将 agentic AI 上下文增长带来的内存需求、CXL 产品和 MemoryAI KV cache、一家一级金融机构扩大使用,以及 ClusterWareAI 和 Stratus 系统列为投资逻辑;称 BofA 预计 Advanced Computing 收入至 FY2028 接近翻倍,并因专业内存能力和利润率改善而给予高于 ODM 与内存制造商的估值倍数。
  • 报道列出风险:内存价格大幅回落可能压低收入和利润率、估值倍数可能随价格见顶预期收缩;ODM/OEM 可能获得更多增量增长,neocloud 占比提高也可能推升营运资金需求。

作者观点与证据

文章转述 BofA 的评级、目标价、盈利预测及其投资逻辑,未附银行原始研究报告或估值模型,因此不能从此文独立核验假设。AI 驱动业务占比及同比增速是报道引用的公司季度数据;“战略重要性”、竞争优势、估值溢价和市场反应属于分析师或媒体归纳。文中称股价周五开盘上涨 8%、随后涨幅约 2.2%,属于报道时点的市场描述。

与相关标的的关系

直接涉及 PENG,重点是 BofA 对其 AI 基建、内存业务、FY2028 财务表现和估值的判断。文章还提到 NVIDIA、Dell、金融机构及 neocloud 等生态关系,但未提供足以量化其对 PENG 收入贡献的材料。

时效性与限制

发布日期为 10/09(未给出具体时刻)。目标价与 FY2028 预测是 BofA 观点,并非公司指引;原文没有给出目标价期限、模型敏感性或原始研报。报价涨跌描述对应文章所述周五交易时段,不能视作当前行情。

原文
Penguin Solutions initiated with a Buy from Bank of America on AI memory demand

Penguin Solutions initiated with a Buy from Bank of America on AI memory demand

Proactive

Sat, October 10, 2026 at 12:30 AM GMT+9 2 min read

  • PENG

-0.20%

  • BAC

-0.50%

Penguin Solutions initiated with a Buy from Bank of America on AI memory demand Proactive uses images sourced from Shutterstock Bank of America (BofA) has initiated coverage of Penguin Solutions (NASDAQ: PENG), a memory and AI infrastructure supplier, with a 'Buy' rating and a $100 price objective.

The target equals 18 times the bank's forecast 2028 earnings per share of $5.43.

The bank forecasts revenue of $2.87 billion and earnings per share of $5.43 for financial year 2028, above analyst consensus of $2.66 billion and $5.04, respectively.

AI-driven businesses now account for 78% of sales and grew 141% year on year in the latest quarter.

Penguin's expertise in improving the performance of large AI clusters of graphics processing units (GPUs) and central processing units (CPUs) has made it strategically important to neoclouds, specialist providers of AI computing infrastructure.

Agentic AI, in which software carries out tasks autonomously, requires more memory as context grows, making memory capacity a bigger constraint than computing power in many deployments.

Penguin's Compute Express Link (CXL) products and MemoryAI KV cache servers address that need, and a tier-one financial institution has already expanded its usage.

More memory close to the GPU also reduces time to first token, the delay before a model starts responding, a performance metric that customers increasingly prioritize.

Penguin's decades of experience in high-performance computing, which led to its ClusterWareAI software and Stratus fault-tolerant systems, give it a competitive advantage, while the bank forecasts that Advanced Computing revenue will almost double by financial year 2028.

BofA applies a premium valuation multiple relative to original design manufacturers and memory makers, citing Penguin's specialized memory expertise and improving margins.

The main risk is a significant correction in memory prices, which could reduce revenue and margins, while the valuation multiple could contract if investors expect memory prices to peak in the near term.

Original equipment manufacturers and original design manufacturers could capture more of the incremental growth, while working capital requirements could rise as neoclouds account for a larger share of revenue.

Shares opened 8% higher on Friday before leveling off around 2.2% higher.

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PENG FY2026 全年及第四季度业绩公告

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四季度存储价格与供需分化

重要性4/5 中高

发布时间接近第四季度,且对服务器DRAM、企业级SSD及消费存储分别给出供需和涨幅判断;与PENG存储业务相关,但缺少公司层面的订单证据。

中文摘要

核心结论

TrendForce预计,2026年第四季度AI服务器需求将继续支撑存储价格,但不同品类表现分化:服务器与企业级产品供应偏紧,消费端采购承压;传统DRAM和NAND合约价仍上涨,涨幅整体趋缓,企业级SSD是预计涨幅加速的例外。

重要性评级

评级 4/5|中高。报告发布时间为 09/30(未给出具体时刻),对存储行业第四季度定价、供应分配及企业级SSD需求提供了较细的品类数据;与PENG所处的存储市场直接相关,但没有披露PENG自身订单或定价。

关键事实

  • TrendForce预计第四季度传统DRAM合约价环比上涨10%–15%,NAND Flash合约价环比上涨15%–20%;供应商将先进制程产能优先投向高性能服务器产品,服务器DRAM仍供不应求。组件、封装测试能力及前端产能调度限制供应,部分长期协议客户的涨价幅度可能低于市场平均。
  • 服务器CPU供应改善后,云服务商(CSP)和服务器OEM增加采购,以满足支持AI agent工作负载的一般服务器RDIMM需求。PC端则因成本上升影响笔记本销售,但OEM仍为预期中的2027年供应紧张而积极采购。
  • 企业级SSD位元需求预计2026年同比增长超过80%,AI推理基础设施扩张、训练向实际部署转移、agentic AI带来的实时检索与缓存数据增长,以及向量数据库QLC渗透共同推升需求。供应商扩充QLC和高容量产品,但新增供应大多已被预订,因此企业级SSD是唯一预计第四季度涨幅加速的存储类别。
  • 消费端较弱:PC厂商压低主流机型SSD容量、使用库存满足出货;手机厂商依赖既有eMMC/UFS库存;模块厂因NAND晶圆价格已高而趋于谨慎采购。消费DRAM及图形DRAM价格仍受供应收缩支撑,但涨势预计放慢。

作者观点与证据

这是TrendForce对存储市场的行业研究判断,给出合约价涨幅预测、企业级SSD需求增速及各品类供需描述。文中将agentic AI扩张和AI推理部署视为服务器内存与企业级SSD需求来源;这些属于行业分析与预测,不能视作已实现的销量或单一公司的订单事实。

与相关标的的关系

输入将PENG列为相关标的。其Integrated Memory业务所处的DRAM和存储市场与报告主题直接相关,报告描述的服务器需求、价格及供应约束可作为行业背景;文章没有提及Penguin Solutions,也没有提供其客户、产品组合、合同价格或可实现收入的信息。

时效性与限制

报告发表于 09/30(未给出具体时刻),重点是2026年第四季度展望。其合约价和需求数字是TrendForce预测,结果仍取决于供应、库存、客户采购及终端承受能力,且不同存储品类的方向和涨幅并不一致。

后续跟踪

可观察第四季度DRAM与NAND合约价实际变化、服务器DRAM供需、企业级SSD需求及新增供应兑现情况,并留意PC、手机和模块厂采购是否继续受成本与库存制约。

原文
AI Server Demand Sustains Memory Contract Price Increases in 4Q26, While Consumer-Side Pressure Persists, Says TrendForce

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AI Server Demand Sustains Memory Contract Price Increases in 4Q26, While Consumer-Side Pressure Persists, Says TrendForce

30 September 2026

Semiconductors

TrendForce

  • Conventional DRAM contract prices are projected to grow 10–15% QoQ in 4Q26, while NAND Flash contract prices are expected to increase 15–20%
  • Enterprise SSDs are the only category expected to see price growth accelerate due to strong demand from CSPs

TrendForce’s latest memory industry research reveals that DRAM suppliers continue to prioritize advanced-process capacity for high-performance server products in 4Q26, keeping the overall market undersupplied. However, the pace of contract price increases is expected to moderate, with conventional DRAM prices projected to rise 10–15% QoQ.

Meanwhile, the NAND Flash market is seeing a divergence between accelerating AI demand and weak consumer demand, although prices are still rising across segments. Overall NAND Flash contract prices are projected to increase 15–20% QoQ.

For PC DRAM, notebook inventories are increasingly reflecting higher component costs in the fourth quarter, weighing on end-market sales. Nevertheless, PC OEMs continue to procure aggressively amid expectations that DRAM supply will remain tight in 2027, providing continued support for prices. On the supply side, DRAM suppliers have been fulfilling agreed volumes to PC OEMs and module makers every quarter throughout 2026. However, the PC DRAM supply could decline in 2027 as more capacity shifts to server applications.

In the server DRAM market, improving server CPU availability has prompted CSPs and server OEMs to increase bit procurement from suppliers to meet growing RDIMM demand for general servers supporting agentic AI workloads. Memory suppliers have increased the share of lower-capacity RDIMMs in their product mix to better address customer demand. However, constraints in component supply, packaging and testing capacity, and front-end DRAM production flexibility continue to prevent supply from fully aligning with demand. Server DRAM is therefore expected to remain undersupplied in 4Q26. On the pricing front, increases for some suppliers are expected to lag the market average due to pricing mechanisms stipulated in long-term agreements (LTAs).

For mobile DRAM, rising costs and the depletion of low-cost inventories continue to weigh on smartphone production, suppressing overall bit demand. At the same time, strong demand and higher margins in AI-related applications are prompting suppliers to continue shifting capacity toward server products, leaving mobile DRAM allocations tight. Consequently, the upward price trend remains firmly in place. However, because substantial increases were already priced in during previous quarters, growth is expected to moderate in 4Q26.

In graphics DRAM, some PC brands are reconsidering GDDR6 as a means of reducing overall BOM costs. GDDR7 demand, meanwhile, continues to be driven primarily by AI chips, with procurement momentum outperforming the PC market.

On the supply side, suppliers continue to scale back GDDR6 production while gradually exiting the market. GDDR7 output also remains limited as advanced-process capacity is increasingly allocated to applications such as server DRAM. These constraints are expected to keep graphics DRAM prices on an upward trajectory.

For consumer DRAM, recent incremental demand has come primarily from SSD customers, supplementing existing demand from consumer networking and TV applications. In particular, the improved outlook for QLC SSD demand has increased corresponding demand for DRAM components. However, major suppliers remain committed to production cuts, leaving the market heavily reliant on Taiwanese suppliers. Prices are therefore expected to continue rising—albeit at a slower pace.

Turning to client SSDs, PC OEMs can rely on finished-goods inventories and channel stock built during the first half of the year to support fourth-quarter shipments, limiting procurement largely to components in short supply. PC brands are also reducing SSD capacities in mainstream models to lower BOM costs, resulting in declines in both procurement volumes and average capacity per device. With buyers holding sufficient inventories and purchasing requirements remaining limited, NAND Flash suppliers have adopted a more flexible pricing stance, restraining the magnitude of price increases.

The enterprise SSD market presents a markedly different picture. Bit demand for enterprise SSDs is projected to grow by more than 80% YoY in 2026 as CSPs continue to expand AI inference infrastructure. Demand is also shifting from LLM training toward actual AI deployment. Additionally, the large-scale rollout of agentic AI is driving exponential growth in data volumes for real-time retrieval and caching, while QLC penetration in vector databases continues to rise.

In response, suppliers are expanding QLC capacity and the availability of high-capacity products, while PCIe 6.0 adoption is ramping alongside next-generation AI server platforms. However, most incremental supply has already been committed, leaving little available on the open market. Enterprise SSD price growth is therefore expected to accelerate in 4Q26, making it the only memory product category to see a larger increase than in the previous quarter.

For eMMC/UFS, smartphone brands—the primary source of demand—are largely relying on existing inventories to support fourth-quarter production, resulting in very limited incremental procurement from suppliers. Lower-margin products such as TVs, set-top boxes, and wearables have even less room to absorb higher prices.

Although suppliers are seeking broader price increases on the back of strong AI-driven pricing momentum, OEMs are responding with only the minimum purchase volumes needed to maintain supplier relationships. As a result, negotiations have effectively narrowed to price confirmation for relatively small order volumes.

In the NAND Flash wafer market, prices have reached historical highs that downstream retail channels are increasingly unable to absorb. This has prompted module makers—the primary buyers—to adopt a more cautious procurement stance.

On the supply side, suppliers continue to prioritize capacity based on profitability, keeping overall wafer availability constrained. However, weak purchasing momentum among module makers is expected to limit overall price growth to a modest increase.

For more information on TrendForce’s semiconductor reports and market data, please visit the Report Page , leave a Message , or Email (SR_MI@trendforce.com) the Sales Department.

For more on the latest technology industry news and trends, please visit News .

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PENG 披露新 CFO 诱导性股权奖励

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PENG第三季度业绩与AI业务展望

重要性4/5 中高

内容直接覆盖PENG季度业绩、分部增长、现金流与管理层展望,证据密度高;但电话会距今数月,FY2027数字仍属初步规划。

中文摘要

核心结论

Penguin Solutions管理层将第三财季增长归因于AI基础设施和Integrated Memory需求,披露营收、利润及存储业务显著增长,并上调FY2026展望;公司同时给出FY2027营收和非GAAP每股收益约30%增长的初步规划。该规划依赖客户需求、供应和成本假设,管理层称当时仍在制定下一财年计划。

重要性评级

评级 4/5|中高。电话会于 07/07(未给出具体时刻)举行,覆盖PENG直接业绩、分部、现金流、客户及下一财年初步展望,信息密度高;距当前已有数月,前瞻数字应按当时口径理解。

关键事实

  • FY2026第三季度净销售额为4.79亿美元,同比增48%、环比增40%;非GAAP营业利润6400万美元,同比增67%,非GAAP摊薄每股收益0.84美元,同比增79%。AI驱动业务(Integrated Memory与非超大规模AI基础设施)占总销售额74%,同比增长104%。
  • Integrated Memory销售额2.75亿美元,同比增111%、环比增60%,管理层称增长同时来自销量和价格;公司称数据中心相关需求和订单积压强劲。Advanced Computing销售额1.38亿美元,同比增4%,其中非超大规模AI基础设施收入同比增81%,占该分部销售额58%,上年同期为33%。Optimized LED销售额6600万美元,同比增7%。
  • 公司将全财年FY2026净销售增长预期上调至中点同比增长22%,非GAAP摊薄EPS预期为2.60美元;Integrated Memory全年销售增长预期升至90%–95%。公司表示,该年度展望不包括Advanced Computing向超大规模客户销售AI硬件,并计入高毛利Penguin Edge业务逐步退出。
  • 管理层给出的FY2027初步看法为总销售额和非GAAP EPS均较FY2026展望中点增长约30%,并称完整展望计划在下一次业绩电话会上发布。问答中,管理层称Advanced Computing初步增长起点可能为中十几,并指出非超大规模AI基础设施订单转收入周期约3–6个月;这些均属初步判断。
  • 营运资金随增长扩大:应收账款7.04亿美元、库存4.98亿美元;季度经营活动现金流为负7500万美元,管理层称主要用于支持存储和AI基础设施增长。期末现金、现金等价物和短期投资合计4.40亿美元。
  • CFO Nate Olmstead宣布离任,Aaron Johnson将任临时CFO,公司启动正式继任者搜寻。管理层称过渡不改变运营模式和策略。

作者观点与证据

这是公司管理层和CFO在业绩电话会上的陈述,包含季度业绩口径、分部数据、管理层解释及前瞻预测。季度财务数字为公司披露;“AI需求更具持久性”、客户积压、平台竞争力及FY2027增长展望属于管理层判断或预测。问答中的分析师假设不是公司确认的数据,公司回答明确表示单一超大规模客户并非FY2027增长展望的重要来源。

与相关标的的关系

文章直接讨论NASDAQ上市公司Penguin Solutions(PENG),披露其Integrated Memory、Advanced Computing和LED业务表现、客户拓展及财务展望。管理层称其AI基础设施客户覆盖企业、NeoCloud和主权AI项目;但公司同时提及供应链约束、较长交付周期、较高存储成本可能影响需求与毛利率。

时效性与限制

电话会日期为 07/07(未给出具体时刻),内容反映FY2026第三季度和当时的管理层预期,不应当作当前季度结果或已兑现的FY2027指引。公司明确称FY2027观点为初步规划;FY2026与FY2027展望受客户需求、宏观环境、供应链、交期和存储成本影响。文中非GAAP指标需结合公司发布的GAAP对账资料阅读。

后续跟踪

原文支持关注后续正式FY2027指引、Integrated Memory的价格与销量贡献、存储及关键组件供应和交期、AI基础设施订单转收入节奏、营运资金和经营现金流,以及CFO继任安排。

原文
Penguin Solutions (PENG) Q3 2026 Earnings Call Transcript & Audio

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Penguin Solutions, Inc. (PENG)

NASDAQ: PENG · Real-Time Price · USD

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80.33

+1.17 (1.48%)

Oct 13, 2026, 9:54 AM EDT - Market open

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Earnings Call: Q3 2026

Jul 7, 2026

  • Slides
  • Earnings release
  • Quarterly report

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Summary

Record Q3 results driven by AI demand led to raised FY 2026 outlook for net sales and EPS. Memory and non-hyperscale AI infrastructure segments saw exceptional growth, with strong backlog and favorable pricing. Preliminary FY 2027 guidance anticipates ~30% growth in sales and EPS.

Operator

Welcome to the Penguin Solutions third quarter fiscal 2026 earnings call. After today's prepared remarks, we will host a question and answer session. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, press star one again. I will now hand the conference over to Suzanne Schmidt with Investor Relations.

Suzanne Schmidt

Head of Investor Relations, Penguin Solutions

Thank you, operator. Good afternoon, and thank you for joining us on today's earnings conference call and webcast to discuss Penguin Solutions' third quarter fiscal 2026 results. On the call today are Kash Shaikh, Chief Executive Officer, and Nate Olmstead, Chief Financial Officer. You can find the accompanying slide presentation and press release for this call on the investor relations section of our website. We encourage you to go to the site throughout the quarter for the most current information on the company. I would also like to remind everyone to read the note on the use of forward-looking statements that is included in the press release and the earnings call presentation.

Please note that during this conference call, the company will make projections and forward-looking statements, including but not limited to statements relating to statements about the company's growth trajectory, financial outlook, and preliminary expectations for future fiscal periods, business plans and strategy, product development and innovation, market demand and shifts, supply chain conditions and cost assumptions, leadership transitions, strategic agreements, and existing and potential collaborations. Forward-looking statements are based on current beliefs and assumptions and are not guarantees of future performance and are subject to risks and uncertainties, including, without limitation, the risks and uncertainties reflected in the press release and the earnings call presentation filed today, as well as in the company's most recent annual and quarterly reports.

The forward-looking statements are representative only as of the date they are made and except as required by applicable law, we assume no responsibility to publicly update or revise any forward-looking statements. We will also discuss both GAAP and non-GAAP financial measures. Non-GAAP measures should not be considered in isolation from, as a substitute for, or superior to our GAAP results. We encourage you to consider all measures when analyzing our performance. A reconciliation of the GAAP to non-GAAP measures is included in today's press release and the accompanying slide presentation. With that, let me now turn the call over to Kash Shaikh, CEO. Kash?

Kash Shaikh

CEO, Penguin Solutions

Good afternoon, thank you for joining our third quarter fiscal 2026 earnings call. Penguin Solutions delivered an exceptional quarter, with record results that reflect disciplined execution and strong customer traction for our AI factory platform strategy. At the company level, we delivered record net sales and significantly higher than anticipated EPS. Building on our excellent third quarter, we are again raising our full-year outlook for both net sales and EPS. All of our business units performed well. Growth was outstanding in our AI-driven businesses. In Q3, our AI-driven businesses represented 74% of total company net sales and grew 104% year-over-year. These businesses consist of Integrated Memory and non-hyperscale AI infrastructure solutions. In these businesses, AI-driven demand continued to outpace net sales growth, contributing to a growing backlog.

This gives us confidence that the AI opportunity is expanding as enterprises increasingly adopt agentic AI workloads powered by inference at scale. Our results this quarter demonstrate that Penguin Solutions is evolving into a leading AI factory platform company. We believe we are still in the early stages of a significant long-term profitable growth opportunity. Before discussing our performance in more detail, I want to address our finance leadership transition. As announced on June 1st, Nate Olmstead will step down as Chief Financial Officer on July 8th to pursue an opportunity in a different industry. Nate has been an important partner during a meaningful period of transformation for Penguin Solutions. His leadership helped strengthen our financial and operational foundation, and I want to thank him for his contributions and wish him well.

I am also pleased that Aaron Johnson, our Vice President of Finance and Accounting, will serve as interim CFO effective July 9th. Aaron brings more than 16 years of public company experience in the technology sector and deep understanding of our business. We have also initiated a search for a permanent CFO. This transition does not change our operating priorities, financial discipline, or focus on execution. Today, I'll cover four key topics before reviewing our third quarter performance. First, the market environment as AI moves into production scale inference and agentic AI. Second, how our AI factory platform is positioned to address this opportunity. Third, the customer and partner momentum that validates our strategy. Fourth, how we are executing across product innovation, operations, and go-to-market.

Since our April earnings call, the demand environment has strengthened as AI continues to transition from early prompt and response experimentation to production scale inference and agentic AI. A simple way to think about the shift is that early AI answered questions, while agentic AI performs work. In the early phase of AI adoption, many workloads were transactional. A user asked a question, AI generated an answer, and the session ended. Agentic AI is different. Agents are persistent, context-rich, and task-oriented. They can operate continuously across workflows, applications, and data sources, moving AI from an advisor to an operator. For example, in software development, each engineer can now have multiple agents that write, test, review, document, and monitor code. In business operations, agents can monitor customer activity, analyze supply chain risk, prepare follow-ups, and update workflows.

As inference workloads scale and agentic AI deployments accelerate, infrastructure requirements for production AI environments continue to expand across full AI data center technology stack. Demand is increasing not only for GPUs and accelerator-attached high bandwidth memory or HBM, but also for general purpose compute, including CPU as well as memory storage and networking. Every GPU deployment depends on a surrounding layer of general purpose compute and memory to feed data, coordinate workflows, manage context, and connect agents to enterprise applications. We believe this trend is strengthening demand for our memory solutions. These persistent context-rich workloads require more memory capacity, faster access to context, and better orchestration. As AI moves to inference at scale, the industry is increasingly recognizing that memory, not compute alone, is becoming one of the primary bottlenecks for large context AI inference performance.

We designed our MemoryAI appliances specifically to address this bottleneck and improve both inference performance and token economics. We believe that for enterprises, sovereign AI initiatives, and new cloud customers, AI factories are becoming essential for optimizing time to first token performance and token economics for inference. These developments reinforce the strategic premise behind Penguin Solutions. Production scale AI infrastructure requires a full stack AI factory platform approach. Our platform connects AI infrastructure, memory, operation software, and operational execution to help customers deploy and operate at scale. Our second topic today is how Penguin is becoming a leading AI factory platform company at the intersection of memory and AI infrastructure. Our platform combines five core elements with our partner ecosystem. First, ClusterWareAI, operating system software for AI factories. Second, MemoryAI and integrated memory solutions. Third, advanced computing systems under our Compute AI brand.

Fourth, OriginAI factory architectures. Fifth, end-to-end design, build, deploy, and managed services. We believe our platform differentiation comes from combining these proprietary products and services into an integrated offering. Customers increasingly need more than just AI hardware procurement. They need architecture that performs in production at scale, an accelerated deployment path, and an operating model that helps them generate profitable revenue or deliver operational efficiencies. In production AI, time to production deployment is directly tied to time to revenue, and our platform is designed to compress both. Customers need the ROI and superior token economics that our Penguin AI factory platform can provide. Turning to our third topic, customer and partner momentum. With our land and expand go-to-market strategy, we continue to add new customer logos and deepen engagements with existing customers. In Q3 2026, we added four new AI infrastructure customer logos.

New logo acquisitions are important in part because they often lead to repeat business. For example, across four trailing quarters from Q3 2025 to Q2 2026, we added 13 new AI infrastructure logos, and seven of those customers have already increased their business with us. New customer engagements can involve longer sales cycles, but they also support deeper customer relationships, repeat business, and more durable long-term growth. Deepgram is a strong example of the power of our partner model and technical differentiation. Penguin designed and deployed an optimized inference environment built with Dell infrastructure and NVIDIA technology to support enterprise voice AI workloads. In Q3, we expanded our engagement with Deepgram to support additional production capacity as its business scales, and Deepgram also acquired our ClusterWareAI product and additional services. In Q3, we also expanded our previously disclosed engagement with a tier 1 financial institution.

This expansion includes our Compute Express Link or CXL-powered MemoryAI KV cache server, ClusterWareAI software, and services with Dell providing AI compute. Deployments such as Haein in South Korea, which provides local Neocloud CPU-as-a-service, continue to demonstrate our ability to support AI infrastructure requirements across a range of customer environments. Thus, our recent Q3 customer win with a leading quantitative trading firm. Another important proof point is Spectra, a new sovereign deployment. This supercomputer system was built and deployed through our collaboration with Sandia National Laboratories and NextSilicon. For Penguin, Spectra demonstrates our ability to design and deploy a complex system in a demanding national security environment. In our memory business across trailing four quarters from Q3 2025 to Q2 2026, we added 16 new logos, and five of those customers subsequently increased their business with us.

We also saw continued expansion with a generative AI customer that is purchasing our CXL memory expansion cards to support inference workload solutions. These relationships validate our land and expand strategy. Once a customer experiences the value, performance, and ROI of our AI factory platform, we have an opportunity to deepen and scale our relationships. Our partner ecosystem continued to recognize Penguin's capabilities. We were recently named an NVIDIA AI Factory Specialized Partner, a recognition of our expertise in designing, building, deploying and managing full stack NVIDIA-based AI factory infrastructure. We were also recognized this quarter as the 2026 Dell Technologies Global Alliances Americas AI Partner of the Year. Let me share some insights into our product operations and go-to-market execution.

In our product portfolio, we continue to invest in the areas where we see potential for durable differentiation, including our MemoryAI products, our ClusterWareAI operating system software, and our OriginAI reference architectures. MemoryAI is focused on addressing the memory bottleneck that emerges as AI inference workloads become contextually larger, more concurrent, and more latency sensitive. Our CXL-based MemoryAI KV cache server is designed to keep large context inference closer to the workload, allowing large language models to respond faster without needing to reprocess entire datasets for every prompt. MemoryAI KV cache can dramatically improve AI factory efficiency by enabling superior token economics for inference and agentic AI workloads, delivering, first, up to 2x higher inference performance. Second, up to 8x lower time to first token latency.

Third, expanded memory capacity beyond GPU HBM by leveraging CXL memory that is approximately 4x-5x more cost-effective than GPU HBM. The tier 1 financial services customer we added in Q2 purchased additional MemoryAI KV cache servers in Q3 for their on-prem AI factory, which is initially focused on inference and agentic AI for code generation using open weight LLMs. We see AI-assisted code generation as a common use case for inference-focused on-premise AI factories, where the right architecture can deliver superior token economics. Our early investment in Celestial AI, a pioneer in Photonic Fabric technology, reflects our longstanding focus on memory architecture innovation. We continue to advance our MemoryAI Photonic Memory Appliance, or PMA, through our continued partnership with Celestial AI team, now part of Marvell. This appliance is designed to extend memory capacity and bandwidth for large context AI inference environments.

Together, our CXL memory expansion cards, CXL-based MemoryAI KV cache server appliance, and MemoryAI PMA appliance reinforce our innovation agenda and long-term vision for AI memory infrastructure. ClusterWareAI addresses another critical need: managing and orchestrating complex AI infrastructure across heterogeneous environments. ClusterWareAI is a hardware vendor agnostic operating system for AI infrastructure in the data centers. It acts as a unified control plane across GPUs, CPUs, memory, and networking, allowing the infrastructure to operate as a single cluster while reducing manual configuration and supporting faster time to production. We recently introduced a new AI factory operations agent, which provides data center administrators with a conversational natural language interface for operational insights. This is the first in a planned family of agents designed to simplify and automate cluster operations and increase administrator productivity with a human-in-the-loop approach.

OriginAI brings these technologies into validated reference architectures that can reduce deployment complexity and risk. Faster deployment can mean faster time to revenue for our customers. We recently introduced OriginAI Inference Solutions. Together, these solutions are designed to leverage Penguin Solutions' four billion hours of GPU runtime experience. They also draw on more than 30 years of expertise delivering advanced memory and AI infrastructure solutions. As our deployments continue to scale across enterprise, sovereign AI, and Neocloud customers, our focus is on increasing repeatability. This includes architectures, software, services, and operations. Over time, this can strengthen both customer outcomes and platform economics. Within Penguin, we are applying agentic AI across our own operations with a focus on measurable business outcomes that support our profitable growth plans.

This includes AI-assisted software development, often referred to as vibe coding, and AI-powered productivity tools designed to accelerate our product cycles, improve time to market, and make our supply chain more scalable and responsive. We are also managing supply chain, working capital, and growth investments with discipline as we scale to meet AI-driven demand. Turning to our third quarter performance, we delivered record quarterly net sales of $479 million, up 48% year-over-year and 40% sequentially. Non-GAAP operating income was a third quarter record at $64 million, up 67% from the year ago quarter, demonstrating the strong operating leverage in our model as we scale. AI-driven demand continued to drive growth in Integrated Memory and in the non-hyperscale AI infrastructure business within Advanced Computing. Together, these two businesses represented 74% of total company net sales and grew 104% year-over-year.

Advanced Computing net sales total $138 million, representing 29% of total company and growing 4% year-over-year. Demand and customer engagement of our AI infrastructure business within Advanced Computing remains very strong across enterprise, sovereign AI, and Neocloud customers. Our non-hyperscale AI infrastructure business is scaling rapidly with third quarter net sales up 81% year-over-year. In Q3, this non-hyperscale AI infrastructure business represented 58% of total Advanced Computing net sales, versus 33% in the third quarter of last year. The shift to inference at scale with agentic AI aligns directly with Penguin's AI factory platform strategy and the differentiated role we play at the intersection of memory and compute infrastructure. Our MemoryAI appliance continues to gain traction. It generated both revenue and new bookings this quarter. The pipeline also continues to strengthen across enterprise, sovereign AI, and new cloud customers.

Memory business net sales were outstanding at $275 million, up more than 111% year over year, supported by both higher volume and pricing. Our strong results reflect a fundamental shift. Agentic AI is driving sustained structural demand for memory, reinforcing our view that this AI-driven demand is more durable than a traditional cyclical memory upturn. We are anchoring our memory business on data center market, where demand is driven by agentic AI. We are growing our engagements across networking, hyperscale infrastructure, and enterprise computing customers for their data center products. We exited the third quarter with a very strong backlog. This reflects robust data center-focused AI demand. This demand continues to outpace net sales growth, giving us a strong backlog entering the fourth quarter. Just as important, we are broadening that growth across a wider set of memory customers. This is creating a more durable customer base.

This momentum reinforces the expanding reach of our data center-focused memory solutions. It also highlights the strength of the opportunity ahead. Our CXL memory expansion cards continue to gain traction, generating both revenue and new bookings this quarter while our customer pipeline continues to strengthen. Our LED business also performed well this quarter, with Q3 net sales totaling $66 million, up 7% year over year. Our strategic priorities remain centered on memory and AI infrastructure within Advanced Computing. At the same time, LED continues to be managed with discipline. It generates positive cash flow and continues to execute against its innovation roadmap. On June 1st, we announced an improved financial outlook. At that time, we expected full year fiscal 2026 net sales and diluted EPS to be at the high end of our previously issued ranges.

Today, based on our third quarter results and supported by strong AI-driven demand, we are further increasing our fiscal 2026 outlook for both net sales and diluted EPS. Nate will provide more details in a moment. Looking ahead, we intend to continue balancing growth investments with operational discipline. Our priorities are clear. First, invest in differentiated software, AI memory, and compute infrastructure solutions. Second, execute with discipline and speed. Third, add new logos and deepen existing customer relationships. Fourth, grow our partner ecosystem and diversify our customer base. Together, these actions are designed to support more consistent and predictable profitable growth. In closing, AI is entering a production phase. This phase is reshaping both architecture and economics of data centers. As agentic AI makes inference more persistent and demanding, several capabilities are becoming more essential. These include memory, orchestration, full stack integration, and operational discipline.

We believe we are strategically positioned at the intersection of memory and AI infrastructure to lead the next phase of AI, inference at scale powering agentic AI workloads. We are still in the early innings of a very large growth opportunity. Thank you to our employees, customers, and partners. Our teams are energized by the opportunity ahead, and we appreciate their continued focus on execution and customer delivery. With that, I will turn the call over to Nate for a closer look at our financial results and outlook

Nate Olmstead

CFO, Penguin Solutions

Thanks, Kash. I will focus my remarks on our non-GAAP results, which are reconciled to GAAP in our earnings release tables and in the investor materials available on our website. With that, let me now turn to our third quarter performance. In the quarter, both net sales and profits were significantly higher than expected, driven primarily by accelerating AI-driven demand for our memory products and continued adoption of AI infrastructure solutions, which drove a 40% sequential increase in our overall net sales and 42% sequential increase in our non-GAAP operating income. For Q3 FY 2026, total Penguin Solutions net sales were a record $479 million, up 48% year-over-year. Non-GAAP gross margin came in at 28.1%, above our expectations and down 3.6 percentage points versus Q3 last year.

Non-GAAP operating margin was 13.4%, up 1.5 percentage points versus last year, non-GAAP diluted earnings per share were $0.84, up 79% year-over-year and up 62% versus last quarter. In the third quarter of fiscal 2026, our overall product net sales were $414 million, representing 87% of total company net sales and growing 60% versus the prior year. Services net sales totaled $65 million or 13% of total net sales and were down 1% versus the prior year. Net sales by business segment were as follows. In Advanced Computing, Q3 net sales were $138 million, representing 29% of total company net sales and grew 4% year-over-year and 19% sequentially. This sales increase reflects stronger AI infrastructure sales to enterprise customers, which more than offset reduced sales to hyperscale customers.

Within Advanced Computing, our non-hyperscale AI infrastructure business continues to scale rapidly, with net sales up 81% year-over-year in the quarter. In addition to accelerating growth in this part of our business, we continue to make good progress on diversifying our net sales to new customer categories. In Q3, the non-hyperscale AI infrastructure business represented 58% of total Advanced Computing net sales versus 33% in the third quarter of last year. We continue to see strong demand from enterprise, NeoCloud, and sovereign AI customers and expect these customer categories to represent an increasing share of our Advanced Computing net sales over time. In Integrated Memory, Q3 net sales were $275 million, representing 57% of total company net sales, up 111% year-over-year and 60% sequentially.

In Optimized LED, Q3 net sales were $66 million, representing 14% of total company net sales and were up 7% versus the same quarter last year. Non-GAAP gross margin for Penguin Solutions in the third quarter was 28.1%, down 3.6 percentage points year-over-year and down 3.1 percentage points sequentially, primarily attributable to the ongoing wind down of our Penguin Edge business and a shift in the overall mix of sales across our business units. These factors were partially offset by AI-driven demand, which supported favorable pricing in our Integrated Memory business during the quarter. Non-GAAP operating expenses for the third quarter were $70 million, up 9% year-over-year and up 14% versus last quarter.

The increase in operating expenses sequentially is due to normal seasonality, increased investments in R&D, including for our ClusterWareAI AI software and MemoryAI solutions, and higher variable compensation as a result of our strong net sales and profit performance. Q3 non-GAAP operating income was $64 million, a third quarter record, up 67% year-over-year and up 42% sequentially. Operating margins were up 1.5 percentage points versus the prior year and 0.2 points sequentially, driven by strong operating leverage. Non-GAAP diluted earnings per share for the third quarter were $0.84, up 79% versus Q3 last year and up 62% versus the prior quarter. Adjusted EBITDA for the third quarter was $68 million, up 51% year-over-year and up 34% versus the prior quarter. Turning to the balance sheet.

For working capital, our net accounts receivable totaled $704 million compared to $293 million a year ago, with the increase primarily driven by significantly higher memory sales volumes and prices. Days sales outstanding remained healthy at 53 days, up from 47 days a year ago and up from 50 days last quarter. Inventory totaled $498 million at the end of the third quarter, up from $184 million a year ago, reflecting increased memory costs, growth in our memory and AI infrastructure businesses, and strategic purchases to maximize supply for anticipated future memory demand. Days of inventory were 42 days, up from 36 days a year ago and down from 51 days last quarter, primarily due to the timing of receipts and shipments.

Accounts payable were $736 million at the end of the quarter, up from $272 million a year ago, due primarily to higher memory costs, growth in our memory and AI infrastructure businesses, and the timing of purchases and payments. Days payable outstanding were 62 days compared to 53 days last year and 63 days last quarter. The year-over-year and quarter-over-quarter movements were due to the timing of purchases and payments. Despite the significant growth in our net working capital, our cash conversion cycle remained within our typical range at 33 days, an improvement of five days compared to Q2, and up three days versus last year due to the timing of purchases and payments.

Consistent with past practice, days sales outstanding, days payables outstanding, and inventory days are calculated on a gross sales and gross cost of goods sold basis, which were $1.22 billion and $1.09 billion, respectively, in the third quarter. As a reminder, the difference between gross and net sales is primarily related to our memory business' logistics services, which are accounted for on an agent basis, meaning that we only recognize the net profit on logistics services as net sales. Cash, cash equivalents, and short-term investments totaled $440 million at the end of the third quarter, down $295 million from Q3 last year and down $49 million sequentially. The year-over-year decrease was primarily due to proceeds from the issuance of preferred shares in Q2 of last year, offset by debt repayments for our term loan in Q4 of last year.

Sequentially, the cash decrease was primarily due to investments in working capital to fund our growth and partially offset by approximately $40 million received from proceeds from the disposition of our 19% equity investment in Zilia Technologies. Third quarter cash flows used for operating activities totaled $75 million, compared to $97 million provided by operating activities in the prior year quarter. The decrease in cash flow in the quarter versus last year was due primarily to investments in net working capital to support the significant growth in our memory and AI infrastructure businesses. For those of you tracking capital expenditures and depreciation, capital expenditures were $3 million in the quarter, and depreciation was $5 million for the quarter. Wrapping up our cash flow activities, we spent $9 million to repurchase approximately 466,000 shares in the third quarter under our stock repurchase program.

As of May 29th, 2026, an aggregate of $56 million remained available for the repurchase of our common stock under our current authorizations. Now turning to our outlook. Given our solid performance over the first nine months and an improved Q4 outlook for our memory business, we are raising our full company net sales and non-GAAP diluted EPS outlook for the year, which at the midpoint now calls for 22% net sales growth and $2.60 of non-GAAP diluted EPS, up from our previous outlook of 12% net sales growth and $2.15 of non-GAAP diluted EPS. As a reminder, our full-year outlook assumes that we will continue to diversify our customer sales mix and does not include any Advanced Computing AI hardware sales to hyperscale customers.

Also consistent with our assumptions from last quarter, our FY 2026 financial outlook reflects the ongoing wind down of our high-margin Penguin Edge business. We expect sales from this business to essentially cease by the end of fiscal 2026. The combined effect of these two assumptions reduces our FY 2026 growth outlook by approximately 14 percentage points at the total company level year-over-year, and approximately 30 percentage points within Advanced Computing. With that said, our full-year net sales outlook reflects the following full-year growth ranges by segment. For Advanced Computing, we are improving our full-year outlook compared with our previous outlook and now expect net sales to decline 15%-20% year-over-year. As it has previously, this outlook reflects the Penguin Edge and hyperscale hardware sales impacts mentioned earlier.

For Memory, we are increasing our full-year outlook, and we now expect net sales to grow between 90% and 95% year-over-year, driven by continued AI-related demand and a favorable memory market environment. For Optimized LED, we are also improving our full-year outlook and now expect net sales to decline approximately 5% year-over-year. Our non-GAAP gross margin outlook for the full year is now 28.5% ±0.5 percentage points. We adjusted our gross margin outlook up by half a percentage point to account for favorable memory pricing, which helped our Q3 gross margins. Our Q4 outlook assumes less pricing favorability than we experienced in Q3, and we therefore expect some downward pressure on gross margins as we exit the year. Our full-year expectation for total non-GAAP operating expenses has increased to $260 million ±$5 million.

For the full year FY 2026, we now expect a non-GAAP diluted share count of approximately 56 million shares, up from our prior outlook, primarily reflecting the expected dilutive impact from our convertible debt as a result of higher share prices. As a result of this dilutive impact, we expect our non-GAAP diluted share count in Q4 FY 2026 to be approximately 62 million shares. Our non-GAAP full-year diluted earnings per share is now expected to be approximately $2.60 ±$0.05. Our forecasted FY 2026 non-GAAP tax rate is now 20%, down from 22%, reflecting increased pre-tax income in jurisdictions with lower tax rates. Note, our Q3 non-GAAP tax rate was 17.3%, which reflects the year-to-date true-up of this new lower full year tax rate.

While we expect to use this normalized non-GAAP tax rate throughout FY 2026 and beyond, the long-term non-GAAP tax rate may be subject to changes for a variety of reasons, including the rapidly evolving global and U.S. tax environment, significant changes in our geographic earnings mix, or changes to our strategy or business operations. Given the strength of current AI-driven demand trends, we want to give some preliminary color on our initial expectations for net sales and non-GAAP EPS growth in fiscal 2027. Based on our current customer signals and our expectations about ongoing AI-driven demand, our preliminary fiscal 2027 view contemplates both total company net sales growth and non-GAAP EPS growth of approximately 30% from the midpoint of our full year FY 2026 outlook. The EPS outlook factors in the higher share count beginning in Q4 2026 that I mentioned earlier.

This is an initial planning view. We expect to provide a full FY 2027 outlook on our next earnings call. Our outlook for fiscal year 2026 and our preliminary view of FY 2027 are based on the current environment and our current assumptions, including, among other things, assumptions relating to customer demand, the global macroeconomic environment, ongoing supply chain constraints, and supply costs, especially as they relate to our Advanced Computing and Integrated Memory businesses. This includes extended lead times for certain components that are incorporated into our overall solutions, impacting how quickly we can ramp existing and new customer projects and fulfill customer orders. Our outlook also contemplates the industry-wide higher cost for memory, which may slow customer demand for our products and solutions and may lower our gross margins in our Advanced Computing and memory businesses.

We believe the combination of accelerating AI-related demand and expanding enterprise, NeoCloud, and sovereign AI customer base and our disciplined operating model positions Penguin well for continued profitable growth. Please refer to the non-GAAP financial information section and the reconciliation of GAAP to non-GAAP measures tables in our earnings release and the investor materials on our website for further details. With that, operator, we are ready for Q&A.

Operator

We will now begin the question and answer session. Please limit yourself to one question. If you would like to ask a question, please press star one to raise your hand. To withdraw your question, press star one again. We ask that you pick up your handset when asking a question to allow for optimum sound quality. If you are muted locally, please remember to unmute your device. Your first question comes from Katherine Murphy with Goldman Sachs. Please go ahead.

Katherine Murphy

Analyst, Goldman Sachs

Thank you for the question. It was great to see the Integrated Memory guidance raised up to 90%-95% for the full year after the strong results of the quarter. Can you talk about how much of this is driven by the higher pricing environment versus demand for new products that you talk to, like the CXL cards? To ask my follow-up now, you also discussed the broadening customer set for your memory products to hyperscale. Can you elaborate on what exactly you're shipping into that market and how much of this contributes to the preliminary outlook for 30% growth in total company sales for fiscal 2027? Thank you very much.

Nate Olmstead

CFO, Penguin Solutions

Thank you, Katherine. At the high level for our memory business, the higher outlook takes into consideration both the volumes as well as the pricing. This is for the overall business. CXL is a part of our business. It's a new product line that is growing, but it is a part of the business versus majority of the business is the data center-focused products, and these are the products we create specialized memory modules for data center products for our OEM customers. To answer your second question, one of the customer is a hyperscaler, and we provide memory module for their data center product for this hyperscaler. It is a portion of the overall business in terms of having multiple customers, having multiple product, and it's one of the customers that we serve with our memory business.

Kash Shaikh

CEO, Penguin Solutions

Yeah. Kat, let me just add to that. For the FY 2027 view that we gave, it's really not a significant piece of that. This is not some multi-hundred million dollar hyperscale customer. It's just a portion of the portfolio, and it's kind of consistent with the run rate we had in FY 2026.

Katherine Murphy

Analyst, Goldman Sachs

Thank you both very much.

Kash Shaikh

CEO, Penguin Solutions

Thanks.

Operator

Your next question comes from Brian Chin with Stifel. Please go ahead.

Brian Chin

Analyst, Stifel

Hi there. Good afternoon. Thanks for letting us ask a few questions, and best wishes to you, Nate. Maybe for the first question, I appreciate firstly, you are providing a preliminary fiscal 2027 guide for top line overall and EPS. I understand you may not want to hone in too much at this stage, but if we look at your implied fiscal 4Q 2026, maybe memory revenue could approach, let's say, $300 million quarterly. If I just flat line that and it probably grows on pricing, et cetera, next year, that could easily grow mid-30%, maybe more percent year-over-year.

Above sort of that baseline. To say LED maybe is not super growthy in your assumption set for next year. Can you put some guardrails maybe on what the Advanced Computing growth could be in fiscal 2027?

Nate Olmstead

CFO, Penguin Solutions

Yeah. Hey, Brian. It is preliminary, right? We're in the middle of our own planning for next year. We have pretty good visibility, I'd say, into the first half at this point with backlog and customer conversations that we're having. I would say for Advanced Computing, probably mid-teens, something like that, would be kind of the starting point. Of course, we'd still have some of the impact year-over-year from the wind down of the Edge business and kind of the transition away from Meta, but a lot of that would've been relieved. What you're starting to see next year in Advanced Computing is more of the growth coming through from the non-hyperscale AI infrastructure business.

Brian Chin

Analyst, Stifel

Great. That's very helpful. I think Kash and you both referenced a little bit about the dynamic of the order to revenue cycle and Advanced Computing extending somewhat. That's new customers. It's also key components perhaps. I guess, how much of a swing factor do you think that could be in fiscal 2027? Are you being pretty conservative about that in terms of anticipating some continued scarcity of memory or other key components to grow those key segments next year?

Kash Shaikh

CEO, Penguin Solutions

I think the perspective from the overall business, if we look at our Advanced Computing, especially non-hyperscale AI infrastructure business, some of the advantage we are going to have going into the next year is the bookings for, let's say, AI infrastructure business will deliver the revenues or the net sales going into the first half. That would be the advantage. As we discussed in the last earnings call, our bookings to revenue is, for that part of the business, AI infrastructure business within Advanced Computing, is between three to six months. That obviously provides us an advantage going into the next year. We see at least for the first half, same timeframe in terms of bookings to revenue lag of about three to six months.

Brian Chin

Analyst, Stifel

Okay, great. Thank you.

Kash Shaikh

CEO, Penguin Solutions

Thanks, Brian.

Operator

Your next question comes from Ananda Baruah with Loop Capital. Please go ahead.

Ananda Baruah

Analyst, Loop Capital

Yeah, thanks, guys. Congrats on all, and thanks for taking the question. I guess just two, if I could, I'll ask them at the same time. On advanced solutions in the broadening customer base, what are you seeing in terms of engagement from a services capability perspective? What are you doing increasingly for across the new broader customer base? Are you seeing people move towards more of agentic usage? The second question is just as the memory business gets bigger and you guys are hooking into the ecosystem in increasing role, are you guys having to do anything different with the business as you go to market, as it becomes more strategic in any broader context? That's it for me. Nate, great working with you again. I'm sure we'll work together again at some point.

Nate Olmstead

CFO, Penguin Solutions

Thanks, Ananda.

Kash Shaikh

CEO, Penguin Solutions

For our AI infrastructure business within Advanced Computing, services is a strategic advantage for us. As opposed to alternatives where customers may procure hardware and then they have to stitch it together, the value we provide to our customers, we provide full system integration of both our products as well as other products that will include the full AI factories or AI data centers for those customers, including design, build, deploy, and manage. That becomes an advantage for us. In some cases, we are also managing these factories for the customers for three to five years, which again is an advantage for the customers to be able to get the ROI from solution we deliver.

In terms of Integrated Memory business, in terms of our strategic direction, one of the things we have done in the last, I'd say three months and increasingly going forward, focusing it on the data centers, right? The data centers is where the demand is exploding, especially both obviously accelerated compute and then now the general purpose compute and memory because of agent API is driving the demand. Strategically, we are focusing on the data center at the high level in terms of the strategy.

Ananda Baruah

Analyst, Loop Capital

Great. Thanks so much.

Kash Shaikh

CEO, Penguin Solutions

Thank you.

Operator

Your next question comes from Rustam Kanga with Citizens. Please go ahead.

Rustam Kanga

Analyst, Citizens

Great. Thank you all for taking my questions. Congrats on the strong set of results and improved outlook. Nate, sad to see you go, but it sounds like you're certainly going out on a high note. My question is on the enhancements to the ClusterWareAI with the AI factory operations agent. As these capabilities increasingly are able to sort of simplify cluster operations and increase the administrator's productivity, Kash, could you maybe just talk about the relationship between the ClusterWareAI uptake and services adoption, and as these capabilities evolve from more conversational LLM towards more agentic capabilities, is there a potential for sort of further monetization on that ClusterWareAI portion of the business? Thanks.

Kash Shaikh

CEO, Penguin Solutions

Yep. The AI agent that we introduced, it's a first agent in the family of the agents. The way to think about it's obviously helping our services differentiate in terms of providing a software that can create automation for both deployment and monitoring of the infrastructure. That further provides them the value through our services, and we discuss services and advantage for our customers when we provide both product and services.

In terms of creating the agentic experiences with ClusterWareAI AI, the benefit we are going to have is, in cases where we have not fully deployed our solution or in an existing data center or an AI factory, we can have the ClusterWareAI AI provided to the customer so that customer can start managing their existing data center or an AI factory, and realize the efficiency that they may not be getting with the traditional deployment. That creates additional opportunity for us to increase our software revenues over time.

Operator

Your next question comes from Matthew Calitri with Needham & Company. Please go ahead.

Matthew Calitri

Analyst, Needham & Company

Hey, guys. Thanks for taking my questions. Nate, I'll echo my regards. It's been great working with you, and best of luck. I'm curious if you guys saw any change in trends in the memory pricing and supply environment over the last quarter, and if there's any color you can share on how much of the quarterly performance was driven by pricing, and if enterprise customers are starting to balk at prices at all.

Kash Shaikh

CEO, Penguin Solutions

The demand continues to increase. While the prices are going up, and they may stabilize at some point, but we see increased demand. It is, as we discussed earlier, is a combination of if we look at the total demand between the backlog and the revenue that we were able to ship, the demand is increasing and our backlog is increasing.

Matthew Calitri

Analyst, Needham & Company

Okay. That's great to hear. Was there any change to the guidance philosophy with Nate's departure? Is there any update on where you are in the CFO search or what sort of qualities you're looking for in a successor?

Kash Shaikh

CEO, Penguin Solutions

First of all, no change in our operating model or our strategy as a result of the transition. We have a very strong finance team and accounting team, and we are fortunate to have Aaron Johnson be our interim CFO while we look for the permanent CFO, and he's been with the company and he's been with Nate and been working with me. That provides the continuity to the business, and we are doing a formal search with an executive search firm where we are looking at both internal and external candidates. The philosophy will be primarily how do we have the continued focus and disciplined execution as we have had in Q3 and before, and how we look at our business in terms of being very mindful of delivering on our commitments. Nate, no change in strategy and pretty smooth transition from my perspective.

Nate Olmstead

CFO, Penguin Solutions

Yeah, I definitely expect a very smooth transition with Aaron. He's been my right-hand person for the last two years and worked very closely with me on all elements of finance. I expect things to be very smooth.

Operator

This concludes the Q&A. We will now hand the conversation over for closing remarks to Kash.

Kash Shaikh

CEO, Penguin Solutions

Thank you, operator. Our third quarter results demonstrate strong execution in a market with durable AI-driven demand. We are excited about the opportunity ahead. Thank you.

Operator

This concludes today's call. You may now disconnect.

打开原文

PENG 发布 CXL KV cache 服务器

中文摘要不可用。

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CXL扩展内存支持分布式推理

重要性2/5 中低

文章含Penguin联合演示及具体性能数字,但来源是产品供应商,测试方法和商业贡献均未披露,且与PENG证券的直接关联较弱。

中文摘要

核心结论

Astera Labs称,Leo CXL内存控制器及与Penguin Solutions部署的方案可将KV cache扩展到GPU附近的更大容量内存,以缓解推理时GPU内存不足;文章引用的部署结果包括GPU利用率提高75%、推理吞吐量达到2倍,以及Penguin展台演示的3.6倍内存扩展。文章由产品供应商发布,未交代这些性能数字的测试条件。

重要性评级

评级 2/5|中低。文章发布日期为2026 年 3 月(具体日期与时刻未提供),属于产品与应用说明;其中Penguin部署示例与PENG业务存在间接关联,但核心性能数据来自Astera Labs的厂商文章,方法细节有限。

关键事实

  • 作者认为,大模型推理产生的KV cache会随上下文和生成token增加而占用大量GPU内存;将缓存卸载到CPU DRAM或SSD可能受容量和延迟限制,导致重复计算、成本上升或请求处理能力下降。
  • Astera Labs称,其Leo方案与Penguin Solutions部署后实现GPU利用率提高75%、推理吞吐量达到2倍。文章还称,Penguin在GTC展台展示的CXL服务器可将内存扩展至3.6倍,用于加速AI推理KV cache。
  • 文章描述两类部署方式:在GPU服务器内通过CPU与CXL连接扩展内存,或使用通过RDMA访问的共享KV cache服务器连接多台GPU服务器。
  • Leo架构据称可与vLLM、TensorRT-LLM和SGLang等推理框架集成;文章另提到Microsoft Azure M系列虚拟机运行SAP HANA的传统企业应用场景。

作者观点与证据

作者为Astera Labs负责Memory & Storage的副总裁,文章属于供应商技术推广内容。其核心性能数字是厂商对特定Leo部署或展台演示的陈述,原文未给出基准配置、工作负载、对照组、样本数量或独立验证,因此不能直接外推为普遍性能增益或经济回报。

与相关标的的关系

文章提及Penguin Solutions共同部署Leo方案,并展示其CXL服务器;这为PENG的MemoryAI及AI基础设施产品提供了一个合作与演示案例。文中没有披露Penguin的订单规模、收入贡献、客户采用率或合同条款;输入元数据也未将此文直接关联到证券代码。

时效性与限制

文章发布日期为2026 年 3 月(具体日期与时刻未提供),并提及GTC 2026演示。内容反映发布时的产品主张,未提供后续商业部署规模或经第三方验证的结果。

后续跟踪

可关注Penguin方案后续客户部署、产品收入或订单披露,以及厂商是否补充可复现的性能测试条件和不同工作负载下的结果。

原文
Inference Tokenomics: How CXL Memory Expansion Improves AI Economics

Skip to main content Home » Resources » Blog » Inference Tokenomics: How CXL Memory Expansion Improves AI Economics

Inference Tokenomics: How CXL Memory Expansion Improves AI Economics

Amit Golander, PhD. Memory & Storage Associate Vice President

In our last post on Breaking Through the Memory Wall , we explored how CXL memory expansion addresses fundamental constraints in RAG and KV cache management. We expand on how inference application deployment has transitioned to a distributed architecture to address the requirements of large-scale applications, and how CXL-attached memory helps improve capacity and performance while lowering TCO.

Memory Bottlenecks in Distributed Inference

As generative models have grown larger, inference architectures have evolved to distribute workloads across multiple GPUs—separating LLM context processing and generation phases across distinct GPUs to improve performance and minimize costly re-computation. But this scale exposes a fundamental constraint: during inference, LLMs build context encoded into KV caches that grow rapidly as the number of tokens generated increases. This context quickly exhausts precious GPU memory, i.e., the Memory Wall. Without efficient memory expansion, latency rises, costs soar, and the end-user experience suffers.

Memory expansion becomes especially crucial in two everyday use cases:

  • first, offloading heavy contexts during input document processing, where models must digest and reason over substantial information; and
  • second, maintaining context across multiple conversation turns as users, whether human or AI agent, pose follow-on questions or requests.

Without nearby memories to maintain inference context efficiently, the consequences are clear: recompute that wastes hardware and power, inflated operational costs, and compromised end-user experiences.

Why KV Cache Offloading Drives Inference ROI

KV cache offloading addresses this challenge by expanding GPU memory to nearby higher-capacity resources. Traditional distributed inference applications offload KV cache to CPU DRAM or SSDs, but these memory tiers are limited, and may introduce latency, which can degrade user experience and limit the number of inference requests each GPU can handle.

CXL-attached memory in a distributed inference architecture provides a superior alternative: higher performance than traditional storage while maintaining cost-effectiveness. Leo-based solutions deployed with Penguin Solutions demonstrate 75% higher GPU utilization and 2X inference throughput, directly translating into improved inference economics per deployed GPU.

Flexible Deployment Options

Astera Labs’ Leo Memory controllers provide flexible solutions to the KV cache challenge. Organizations can deploy Leo-based memory modules in either:

  • an intra-GPU server configuration , expanding memory via CPU-CXL connectivity within a GPU server, or
  • an inter-GPU server architecture using Leo in shared KV cache servers accessed via RDMA, as demonstrated in Penguin Solutions’ GTC booth ( also see session EX82068 ).

CXL-enabled servers at Penguin Solutions booth 1031 demonstrate 3.6x memory expansion, accelerating KV cache for AI inference workloads and boosting GPU utilization.

This deployment model allows infrastructure architects to build a distributed KV cache infrastructure that serves multiple GPU servers, providing both the capacity and performance needed for large-scale distributed inference without the latency penalties of traditional storage approaches.

Open Ecosystem Integration

Leo’s architecture supports topology-aware deployments that scale with distributed inference workloads, and integrates with existing inference frameworks, including vLLM, TensorRT-LLM, and SGLang, enabling deployment without application-level changes. This open-source compatibility ensures integration with existing AI stacks without costly migration projects.

While Leo’s AI inference benefits are clear, the technology delivers value across diverse applications, including Microsoft Azure’s M-series VM instances running SAP HANA , leverage Leo for memory expansion in traditional enterprise applications.

Looking Forward

As distributed AI inference continues scaling across rack-scale architectures, Astera Labs remains focused on advancing CXL memory solutions that address the core challenge: keeping GPUs fully utilized while managing inference economics. Stay tuned to Astera Labs for future updates on scaling AI infrastructure without hitting the memory wall.

Visit us at GTC 2026: See Leo-based KV cache offload demonstrations at Penguin Solutions booth 1031. Learn how CXL memory expansion can optimize your inference deployment economics.

About Amit Golander, PhD. Memory & Storage Associate Vice President

Dr. Amit Golander has spent decades working on computer infrastructure—building, researching, and pushing the limits of high‑speed storage, compute, memory, and fabrics. He’s led global teams across three startups and four large companies, and has published roughly 90 papers and patents. In his spare time, Amit teaches AI Infrastructure and Storage and mentors M.Sc. students at Tel‑Aviv University.

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