Penguin Solutions, Inc.

Ladies and gentlemen, thank you for joining us and welcome to Penguin Solutions' second 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, Investor Relations. Suzanne, please go ahead.

Thank you, operator. Good afternoon and thank you for joining us on today's earnings conference call and webcast to discuss Penguin Solutions' second 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 about the market demand, technology shifts, industry trends, and the company's growth trajectory and financial outlook, business plans and strategy, including investment plans, product development and roadmap, anticipated sales, orders, revenue, and customer growth and diversification, and existing and potential strategic agreements and 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 gap and non gap financial measures. Non gap measures should not be considered in isolation from as a substitute for or superior to our gap results. We encourage you to consider all measures when analyzing our performance. A reconciliation of the gap to non gap measures is included in today's press release and accompanying slide presentation. And with that, let me now turn the call over to Kash Shaikh, CEO. Kash?

Good afternoon. Thank you for joining our second quarter F526 earnings call. This is my first earnings call as CEO of Penguin Solutions, and I'm excited to step into this role. I want to start by thanking Mark Adams for his leadership and for the strong foundation he built. Since joining in early February, I've spent significant time with customers, partners, and our teams around the world. I've witnessed the strength of the company, both in our technology and our customer relationships. What is clear is this, AI is moving from experimentation to production, with workloads increasingly shifting towards real-time inference. We are already seeing this translate into customer demand beyond hyperscale across enterprise, neocloud, and sovereign AI markets. We expect this transition to expand our addressable market and drive increased demand for integrated AI infrastructure, where Penguin is already winning. We see this firsthand in the breadth of our deployments, from a sovereign AI factory, Hain in South Korea, to enterprise voice AI with Deepgram, to large scale research systems with Georgia Tech, along with a growing pipeline across all three market segments. What makes this opportunity so significant is that the architecture of AI is also changing. Model training was largely compute bound. Inference powering agentic AI is memory bound and latency sensitive. We believe this is driving a re-architecture of the data center across compute, memory, interconnect, and software. We also see AI driving memory demand, not only for the high bandwidth memory or HPM used with GPUs or other accelerators, but also for general purpose memory General purpose compute wraps around every GPU build out. And whether it's reinforcement learning pipeline or inference survey, that workload runs on processors backed by significant memory content across the entire system. So while memory markets are cyclical, we believe AI is adding a more durable layer of demand for memory. As AI factories scale, I expect customers to increasingly prioritize partners that deliver with speed and precision, along with full stack AI factory platform capabilities, including compute, scalable memory systems, cluster management software, end-to-end services, and a partner ecosystem to deliver a differentiated solution. Time to deployment is now directly tied to time to force token. Against this backdrop, we are building Penguin into an AI factory platform company. Our AI factory platform is built around six core elements. First, Penguin Clusterware, our AI infrastructure management software. Second, our new Penguin Memory AI line of systems designed specifically for AI inference workloads. Third, Penguin Advanced Computing Systems optimized for AI workloads. Fourth, Penguin Origin AI factory architectures, our reference designs for AI factories. And fifth and sixth, end-to-end services and our partner ecosystem. Production-grade AI factories require full-stack design across compute, memory, storage, networking, and software. We partner with leading AI companies, including Nvidia and SK Telecom, and partners like Dell. We also offer complete end-to-end services spanning design, build, deploy, and manage services. We are strategically positioned at the intersection of AI infrastructure and memory, with a long track record in both. Few, if any, companies combine these capabilities at scale. We believe that together our AI infrastructure and memory expertise position us to meet the evolving requirements of AI infrastructure as it shifts towards inference workloads. This supports our ability to develop differentiated solution. Given the momentum we are seeing in our AI infrastructure business, and the significant market opportunity ahead of us. We are very focused in this area. We plan to invest more in our AI factory platform to accelerate our AI business growth, specifically in product innovation, go-to-market, and customer engagement. In March at NVIDIA GTC conference, we announced two AI inference-centric solutions aligned with this strategy. First, the Penguin Memory AI Server. Building upon our Compute Express Link, or CXL-based memory expansion capabilities, we introduced a new line of scalable memory systems called Memory AI. CXL is a high-speed interconnect that enables scalable shared memory across GPUs and CPUs. We also announced the immediate availability of our new memory AI KV cache server. Here KV or key value cache stores inference context to accelerate large language model responses. Second, the expansion of our origin AI factory architecture portfolio, which now includes blueprints that address the larger workloads and the low latency demands of AI inference. We also continue to expand capabilities of clusterware toward a unified control plane for AI factory infrastructure. integrating the open ecosystem to deliver repeatable production scale deployments. To accelerate the innovation and strengthen our leadership team, we recently appointed Ian Coley as Senior Vice President and Chief Product Officer. Ian brings more than two decades of experience building AI infrastructure platforms and scaling high performance computing most recently at Amazon Web Services. He was recently named by HPC Wire to its People to Watch 2026 list, reflecting his reputation in the industry. Now, let me briefly address our second quarter performance. In Q2, we delivered net sales of $343 million. Non-GAAP gross margin was 31.2%. Non-GAAP diluted earnings per share were $0.52. These results reflect strong demand and execution in memory and continued progress in our AI HPC business. Before turning to the segments, I would like to address our updated outlook As Nate will describe in further detail, following our solid Q2 net sales and EPS performance, we are raising the midpoint of our full-year net sales and EPS outlook. We are raising our outlook for our integrated memory business fueled by AI-driven demand, strong execution by our team, and favorable pricing dynamics. While our second half advanced computing net sales outlook is lower than our prior expectations, we are encouraged by strong year-over-year Q2 bookings growth for non-hyperscalar AIHPC business, which included five new AIHPC customer wins, that brings our first half total this year to seven new AI HPC logos compared to three in the first half of last year. With that context, let me turn a closer look at each of the segments. Starting with advanced computing, net sales for the quarter were $116 million. representing 34% of total company net sales and declined year over year. Advanced computing net sales for the second quarter reflect both the timing of large deployments and our transition away from hyperscaler concentration. They also reflect the previously disclosed wind down of our Penguin Edge business. We believe diversification of net sale and wind down of Penguin Edge will strengthen the long-term quality of the business. As I mentioned, we are transitioning our AI infrastructure business from hyperscaler concentration toward a more diversified customer base across enterprise, neocloud, and sovereign AI. This transition is showing very encouraging progress but we still have more work to do. Non-hyperscale AIHPC net sales grew 50% year over year for the first half of the year, representing over 40% of first half segment net sales. Supported by strong non-hyperscale year over year booking growth in the quarter, including five new AI HPC logos across financial services, biomedical research, and energy. We expect further diversification in the second half of the fiscal year. Our AI HPC pipeline continues to strengthen with opportunities to acquire additional logos in the second half of the fiscal year across enterprise, neocloud, sovereign AI customers. As previously discussed, these engagements typically progress over many months from prospecting to design to award, followed by contracting and ultimately system build and deployment. While this sales cycle can be long, often 12 to 18 months and can introduce quarterly net sales variability. It also supports deeper customer relationships, repeat business, and a more durable long-term growth. I'm encouraged by the trajectory of the business and the signals we are seeing in the market. Beyond the numbers, we are also seeing increased activity in specific enterprise verticals. For example, we recently announced our collaboration with Deepgram and Dell to support enterprise voice AI deployments. This win highlights the growing demand for low latency production scale inference infrastructure in real time applications. In this engagement, Penguin designed and deployed an optimized inference environment built on Dell PowerEdge servers and NVIDIA RTX Pro 6000 Blackwell GPUs. This solution facilitates deep-gram speech-to-text, text-to-speech, and voice agent functionalities for applications within healthcare and retail sectors. This case study also demonstrate how design and integration expertise delivers differentiated value. As inference workload scale, we expect these types of deployments to become an increasingly important driver of AI infrastructure demand. Georgia Tech's AI Makerspace developed in partnership with NVIDIA is a strong example. Our relationship with Georgia Tech continues to grow and validates Penguin's ability to help organizations move efficiently from concept to production grade AI infrastructure. Now turning to integrated memory, net sales for the quarter were 172 million, representing 50% of total company net sales and grew 63% year over year. AI-driven demand remains strong across networking, telecommunications, and computing market segments. Pricing dynamics were favorable, and although supply remained tight, We continue to manage constraints effectively through our supplier relationships and disciplined procurement. Stepping back, our AI HPC and memory segments taken together enable us to integrate compute and memory architecture in ways that meet the requirements of production AI environments. Memory architecture is becoming increasingly central to AI performance, particularly as inference workloads scale. Our early investments in CXL position us well as customers evaluate more dynamic memory architectures. Furthermore, we are beginning to see this demand translate into customer deployments including a recent substantial order for CXL cards from a generative AI company building solutions for inference workloads. This reinforces our strategic position at the intersection of memory and AI infrastructure to capitalize on the next phase of AI. focused on inference powering agentic AI workloads. These solutions are sold to enterprise AI infrastructure buyers, the same customers we serve in our AI HPC business. For example, we sold our CXL powered KV cash servers to a tier one financial institution for their on-premise AI factory. In parallel, we continue to advance development of our photonic memory appliance, or PMA, formerly referred to as OMA, which is designed to extend memory capacity and bandwidth for large-scale AI environments. We were an early investor in a photonic memory company, Celestial AI, reflecting our longstanding focus in memory architecture innovation and our early conviction in the importance of optical interconnects for next-generation AI systems. Celestial AI was decently acquired by Marvell in a multi-billion dollar deal. Beyond the portion of proceeds we received from the acquisition as an investor, we are positioning ourselves for future growth in this market. As inference workloads expand, technologies like PMA can help address key memory scaling challenges in the next generation AI systems. Last but not least, LED. Net sales for the quarter were 56 million, representing 16% of total company net sales, and were down 7% year over year. The business continues to operate with focused leadership and dedicated operational discipline. While market conditions remain mixed, we are maintaining a disciplined approach to investment and capital allocation. We are focused on optimizing portfolio value while concentrating resources on areas where we see the strongest long-term returns. In close, the demand for data center AI infrastructure and memory is expanding rapidly. AI factories are becoming infrastructure that powers artificial intelligence across a range of industries. As AI shifts toward inference and agentic systems and scales across large enterprise, new cloud and sovereign AI environments, we expect demand to accelerate. At the same time, memory is becoming a defining constraint. and a defining opportunity. Penguin sits at the intersection of AI infrastructure and memory innovation. And we believe that is a powerful position to be in. Our focus is clear. We are prioritizing four areas. First, to invest in product innovation across our AI factory platform. particularly at the intersection of AI infrastructure and memory to drive profitable growth. Second, to execute with speed and precision. Third, to deepen customer engagement and our ecosystem to support long-term growth. And fourth, to continue diversifying our customer base while building toward more consistent and predictable growth. We believe this focus positions us well to execute in a rapidly evolving market while continuing to build a durable and scalable business. With that, I'll turn it over to Nate.