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Semi Doped

Vikram Sekar and Austin Lyons

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

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  • 25 episodes
  • weekly
  • Avg 47 min
  • English

Episodes 25

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Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • May 4 · 45 min

    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

    • Transcript
  • April 24 · 46 min

    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

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  • April 20 · 39 min

    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

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  • April 17 · 38 min

    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

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  • April 10 · 34 min

    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

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Showing 21–25 of 25 episodes