Semi Doped

Vikram Sekar and Austin Lyons

The business and technology of semiconductors. Alpha for engineers and investors alike.

  1. PŘED 1 DNEM

    CapEx is just Memory Tax Now, Deepseek V4 NAND impact

    The hyperscaler memory tax quarter. More CapEx? Pssh. We knew flops needed scaling. But $25B at Microsoft alone just to pay higher component prices?  A memory tax. That's the news. NAND? Sold out. HBM? Sold out. What we cover: SanDisk revenue +97% sequential.78% gross margin. Guidance above 80% next quarter.Samsung HBM4 first to ship. Demand outstripping supply.DeepSeek v4 goes SSD-centric. KV cache offloads to flash.Microsoft: $25B of 2026 CapEx is just memory pricing.Jassy: memory shortage pushes on-prem to AWS.Qualcomm: mystery custom ASIC. Ships December.New Semi Doped with @vikramskr and @austinsemis. Check out our Substacks - https://www.viksnewsletter.com/  - https://www.chipstrat.com/ Chapters: 0:00 Intro and Vik goes full-time 5:15 Earnings week: the memory tax 7:26 Samsung HBM4 and the Gbps race 14:42 Is the memory tax worth it? 17:37 SanDisk and the SunDisk origin 23:22 78% gross margins and 5-year supply lock-ins 29:29 DeepSeek v4 and SSD-centric inference 38:49 Hyperscaler CapEx and the cloud pull 42:49 AI accelerators: TPU, Trainium, MTIA

    46 min
  2. Masterclass on Google's TPU v8 Networking

    24. 4.

    Masterclass on Google's TPU v8 Networking

    Google's Cloud Next 2026 keynote? Fire. 🔥 The TPU is now two chips instead of one — 8t for training, 8i for inference — but more interestingly, it's two scale-up networking topologies too. Austin Lyons (Chipstrat) and Vik Sekar (Vik's Newsletter) walk through what actually changed, one day after the announcement. OCS? Yes. AECs? Yep. Copper? Yep. Optics? Yep. We cover Virgo (Google's 47 petabit/second scale-out fabric, built entirely on OCS), Boardfly (the new scale-up topology for MoE inference that cuts hop count from 16 to 7), and the 3D torus Google still uses for training. Why is optical circuit switching the substrate of Google's data center? Why do active electrical cables still carry scale-up traffic inside racks? Why did Google split the CPU layer too, with custom ARM Axion head nodes to keep the TPUs fed? Along the way we trace the Dragonfly topology lineage to a 2008 paper by John Kim, Bill Dally, Steve Scott, and Dennis Abts. Abts went on to build Groq's rack-scale interconnect before landing at Nvidia. Chapters:  0:00 Intro  0:21 Two TPUs for two workloads  2:31 HBM, SRAM, and Axion CPUs  7:22 Why networking is the new bottleneck  17:14 Virgo: rebuilding scale-out on optics  25:24 3D torus Rubik's Cube scale-up for training  34:50 Boardfly: scale-up for MoE inference  42:07 Workload-specific everything Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/austinsemis Follow Vik: Newsletter: https://www.viksnewsletter.com/ X: https://x.com/vikramskr

    47 min
  3. Meta VP Matt Steiner on Ads Infra, GPUs, MTIA, and LLM-Written Kernels

    20. 4.

    Meta VP Matt Steiner on Ads Infra, GPUs, MTIA, and LLM-Written Kernels

    Matt Steiner, VP of Monetization Infrastructure, Ranking & AI Foundations at Meta, walks through how Meta's ad system actually works, and why the infrastructure behind it differs from what you'd build for LLMs. We cover Andromeda (retrieval on a custom NVIDIA Grace Hopper SKU Meta co-designed), Lattice (consolidating N ranking models into one), GEM (Meta's Generative Ads Recommendation foundation model), and the adaptive ranking model, a roughly one-trillion-parameter recommender served at sub-second latency. We get into why recommender workloads aren't embarrassingly parallel like LLMs (the "personalization blob"), what that means for Meta's MTIA custom silicon roadmap, and how LLM-written kernels (KernelEvolve) flipped the economics of running a heterogeneous hardware fleet. Demand for software engineering has actually gone up as the price has come down. Meta now wants ~100x more optimized kernels per chip. Read the full transcript at https://www.chipstrat.com/p/an-interview-with-meta-vp-matt-steiner Chapters: 0:00 Intro and scale 0:39 How Meta's ad system works 2:00 Meta Andromeda and the custom NVIDIA SKU 3:30 Lattice: consolidating ranking models 5:00 GEM, Meta's ads foundation model 6:30 Adaptive ranking for power users 8:17 The scale: 3B DAUs at sub-second latency 9:40 Why longer interaction histories matter 10:45 The anniversary gift analogy 12:57 A decade of compute evolution 15:21 Meta's infra as a CP-SAT problem 16:07 Co-designing Grace Hopper with NVIDIA 17:47 Matching compute shape to workload 18:26 Influencing hardware and software roadmaps 20:23 MTIA: why ads aren't LLMs 22:07 The personalization blob and I/O ratios 26:38 One trillion parameters at sub-second latency 28:26 Heterogeneous hardware trade-offs 29:30 KernelEvolve: LLMs writing custom kernels 33:30 GenAI and recommender systems cross-pollination 35:21 The 2-year infrastructure outlook 37:00 Why demand for software engineering is rising 38:53 How Matt stays on top of it all Relevant reading: KernelEvolve (Meta Engineering): https://engineering.fb.com/2026/04/02/developer-tools/kernelevolve-how-metas-ranking-engineer-agent-optimizes-ai-infrastructure/ Follow Chipstrat: Newsletter: https://www.chipstrat.com X: https://x.com/chipstrat

    40 min
  4. Credo + Dust Photonics, XPO, Nuvacore

    17. 4.

    Credo + Dust Photonics, XPO, Nuvacore

    Austin and Vik discuss Credo's acquisition of Dust Photonics, XPO as the new standard for scale-out (maybe instead of CPO?) and some thoughts about Nuvacore entering the CPU scene for agentic AI. Gavin Baker's tweet: https://x.com/GavinSBaker/status/2044410644301046031?s=20 Vik's Substack: https://www.viksnewsletter.com Austin's Substack: https://www.chipstrat.com Chapters 00:00 Introduction to the Semiconductor Landscape 02:49 The Rise of Nuvacore and CPU Innovations 05:27 The Demand for CPUs in the AI Era 07:59 Photonics: The Next Frontier in Semiconductors 10:26 Credo's Acquisition of Dust Photonics 13:12 Vertical Integration in Semiconductor Companies 15:15 The Future of Copper and Optical Technologies 20:28 The Evolution of AI Training Models 25:28 Innovations in Optical Interconnects 31:10 The Future of Data Center Connectivity 36:56 Strategic Implications in the Optical Ecosystem

    38 min
  5. Is Intel Finally Back with a $300B market cap? OpenClaw can Dream?

    10. 4.

    Is Intel Finally Back with a $300B market cap? OpenClaw can Dream?

    In this episode, Austin and Vik discuss if Intel is finally back with CPU partnerships with Google, and heterogeneous inference with SambaNova, while market cap soars above $300B. Vik tries to get his OpenClaw instance to dream every night. Chapters 00:00 Anthropic's New Direction: Chip Development 02:30 Navigating Subscription Changes and Token Costs 05:25 Exploring Alternative AI Models 08:10 The Economics of AI: Rent vs. Buy 10:56 Intel's Resurgence and Market Dynamics 15:23 Intel's Strategic Partnerships and Market Positioning 19:37 The Role of IPUs in Modern Computing 25:08 Coexistence of x86 and ARM Architectures 29:55 Innovations in Chip Architecture and Future Prospects

    34 min
  6. Reiner Pope (MatX): Designing AI Chips From First Principles for LLMs

    9. 4.

    Reiner Pope (MatX): Designing AI Chips From First Principles for LLMs

    Reiner Pope is the co-founder and CEO of MatX, the startup building chips designed from first principles for LLMs. Before MatX, Reiner was on the Google Brain team training LLMs, and his co-founder Mike Gunter was on the TPU team. They left Google one week before ChatGPT was released. A counterintuitive throughput insight from the conversation: “Low latency means small batch sizes. That is just Little’s law. Memory occupancy in HBM is proportional to batch size. So you can actually fit longer contexts than you could if the latency were larger. Low latency is not just a usability win, it improves throughput.” We get into: • The hybrid SRAM + HBM bet, and why pipeline parallelism finally works • Overcoming the CUDA moat • Why frontier labs are willing to bet on an AI ASIC startup • Memory-bandwidth-efficient attention, numerics, and what MatX publishes (and what it does not) • Why 95% of model-side news is noise for chip design • Why sparse MoE drives MatX to “the most interconnect of any announced product” • How MatX uses AI for its own chip design • The biggest challenges ahead Chapters: 00:00 “We left Google one week before ChatGPT” 00:24 Intro: who is MatX 01:17 Origin story: leaving Google for LLM chips 02:21 GPT-3 and the “too expensive” problem 04:25 Why buy hardware that is not a GPU 05:52 Overcoming the CUDA moat 08:46 Early investors 09:35 The name MatX 09:59 The chip: matrix multiply + hybrid SRAM/HBM 12:11 Why pipeline parallelism finally works 14:22 Reading papers and Google going dark 15:20 Research agenda: attention and numerics 17:06 Five specs and meeting customers where they are 19:24 Why frontier labs are the natural first customer 20:32 Workloads: training, prefill, decode 22:18 Little’s law and the throughput case for low latency 24:29 Interconnect and MoE topology 26:35 Inside the team: 100 people, full stack 28:32 Agentic AI: 95% noise for hardware 30:35 KV cache sizing in an agentic world 32:11 How MatX uses AI for chip design (Verilog + BlueSpec) 34:23 Go to market: proving credibility under NDA 35:12 Porting effort for frontier labs 36:34 Biggest skepticism: manufacturing at gigawatt scale 37:32 Hiring plug Austin Lyons @ Chipstrat: https://www.chipstrat.com Vik Sekar @ Vik's Newsletter: https://www.viksnewsletter.com/

    39 min

  7. 7. 4.

    $300M for 70K Viewers | Intel x Elon, OpenAI x TBPN, Citrini's Strait of Hormuz Stunt

    Intel Foundry just partnered with Elon Musk’s Terafab. What is Terafab anyway, why vertically integrated fabs make sense but the economics don’t (yet!), and what Intel is doing here (hint: no idea). Then: OpenAI acquires TBPN for an estimated $100-300M. Not sure why, but the more interesting thing is the value of niche audiences when five companies control a trillion dollars in AI capex. And finally, Citrini Research sent an analyst to the Strait of Hormuz with a Pelican case full of spy gear, $15K cash, and Cuban cigars. The most unhinged research trip in Substack history. Austin Lyons — Chipstrat (https://chipstrat.com)                                                              Vik Sekar — Vik's Newsletter (https://www.viksnewsletter.com) Subscribe for weekly episodes on semiconductors, AI, infrastructure, and the business of chips.

    36 min
  8. NVIDIA's Marvell Strategy, Is Memory Different This Time?, Intel's Ireland Fab

    3. 4.

    NVIDIA's Marvell Strategy, Is Memory Different This Time?, Intel's Ireland Fab

    In this episode, Austin and Vik analyze NVIDIA's $2 billion investment in Marvell NVLink Fusion, exploring its implications for AI infrastructure, interconnect protocols, and the broader chip ecosystem. They also discuss the current memory market surge, DRAM pricing, and Intel's strategic fab buyback, providing deep insights into industry trends and future directions. On Substack Vik: https://www.viksnewsletter.com/ Austin: https://www.chipstrat.com/ Chapters 00:00 NVIDIA's $2 Billion Investment in Marvell 20:11 The Memory Market Crisis 20:16 The Future of Memory Pricing and Consumer Impact 22:55 The Cycle of Supply and Demand in Memory 27:23 AI's Impact on Memory Demand 31:46 Long-Term Agreements and Market Stability 35:07 Intel's Strategic Fab Buyback 40:44 Monopoly Analogy: Intel's Market Strategy

    42 min
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Informace

The business and technology of semiconductors. Alpha for engineers and investors alike.

Informace

  • Autor
    Vikram Sekar and Austin Lyons
  • Aktivní
    2 tis.
  • Díly
    26
  • Hodnocení
    Nezávadné
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    © 2026 Semi Doped
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    Semi Doped
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