Semi Doped

Vikram Sekar and Austin Lyons

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

エピソード

  1. A New Era of Context Memory with Val Bercovici from WEKA

    2日前

    A New Era of Context Memory with Val Bercovici from WEKA

    Vik and Val Bercovici discuss the evolution of storage solutions in the context of AI, focusing on Weka's innovative approaches to context memory, high bandwidth flash, and the importance of optimizing GPU usage. Val shares insights from his extensive experience in the storage industry, highlighting the challenges and advancements in memory requirements for AI models, the significance of latency, and the future of storage technologies. Takeaways Context memory is crucial for AI performance.The demand for memory has drastically increased.Latency issues can hinder AI efficiency.High bandwidth flash offers new storage capabilities.Weka's Axon software enhances GPU storage utilization.Token warehouses can significantly reduce costs.Augmented memory grids improve memory access speeds.Networking innovations are essential for AI storage solutions.Understanding memory hierarchies is vital for optimization.The future of storage will involve more advanced technologies.Chapters 00:00 Introduction to Weka and AI Storage Solutions 05:18 The Evolution of Context Memory in AI 09:30 Understanding Memory Hierarchies and Their Impact 16:24 Latency Challenges in Modern Storage Solutions 21:32 The Role of Networking in AI Storage Efficiency 29:42 Dynamic Resource Utilization in AI Networks 30:04 Introducing the Context Memory Network 31:13 High Bandwidth Flash: A Game Changer 32:54 Weka's Neural Mesh and Storage Solutions 35:01 Axon: Transforming GPU Storage into Memory 39:00 Augmented Memory Grid Explained 42:00 Pooling DRAM and CXL Innovations 46:02 Token Warehouses and Inference Economics 52:10 The Future of Storage Innovations Resources Manus AI $2B Blog: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Also listen to this podcast on your favorite platform. https://www.semidoped.fm/ Check out Vik's Substack: https://www.viksnewsletter.com/ Check out Austin's Substack: https://www.chipstrat.com/

    54分
  2. OpenClaw Makes AI Agents and CPUs Get Real

    5日前

    OpenClaw Makes AI Agents and CPUs Get Real

    Austin and Vik discuss the emerging trend of AI agents, particularly focusing on Claude Code and OpenClaw, and the resulting hardware implications. Key Takeaways: 2026 is expected to be a pivotal year for AI agents.The rise of agentic AI is moving beyond marketing to practical applications.Claude Code is being used for more than just coding; it aids in research and organization.Integrating AI with tools like Google Drive enhances productivity.Security concerns arise with giving AI agents access to personal data.Local computing options for AI can reduce costs and increase control.AI agents can automate repetitive tasks, freeing up human time for creative work.The demand for CPUs is increasing due to the needs of AI agents.AI can help summarize and organize information but may lack deep insights.The future of AI will involve balancing automation with human oversight.Chapters (00:00) Introduction: Why 2026 may be the year of AI agents (01:12) What people mean by agents and the OpenClaw naming chaos (02:41) Agents behaving badly: crypto losses and social posting (03:38) Claude Code as a research tool, not a coding tool (05:54) Terminal-first workflows vs GUI-based agents (07:44) Connecting Claude Code to Gmail, Drive, and Calendar via MCP (09:12) Token waste, authentication friction, and workflow optimization (10:54) Automating newsletter ingestion and research archives (12:33) Giving agents login credentials and security tradeoffs (13:50) Filtering signal from noise with topic constraints (16:36) AI-driven idea generation and its limitations (17:34) When automation effort is not worth it (19:02) Are agents ready for non-technical users? (20:55) Why OpenClaw should not run on your personal laptop (21:33) Safe agent deployment: VPS vs local servers (23:33) The true cost of agents: infrastructure plus inference (24:18) What OpenClaw adds beyond Claude Code (26:53) Agents require managerial thinking and self-awareness (28:18) Local inference vs cloud APIs (30:46) Cost control with OpenRouter and model hierarchies (32:31) Scaling agents forces model and cost optimization (33:00) AI aggregation vs creator analytics (35:58) AI as discovery, not a replacement for reading (38:17) When summaries are enough and when they are not (39:47) Why AI cannot understand what is not said (41:18) Agentic AI is driving unexpected CPU demand (41:49) Intel caught off guard by CPU shortages (44:53) Security, identity, and encryption shift work to CPUs (46:10) Closing thoughts: agents are real, early, and uneven Deploy your secure OpenClaw instance with DigitalOcean: https://www.digitalocean.com/blog/moltbot-on-digitalocean Visit the podcast website: https://www.semidoped.fm Austin's Substack: https://www.chipstrat.com/ Vik's Substack: https://www.viksnewsletter.com/

    48分
  3. An Interview with Microsoft's Saurabh Dighe About Maia 200

    1月28日

    An Interview with Microsoft's Saurabh Dighe About Maia 200

    Maia 100 was a pre-GPT accelerator. Maia 200 is explicitly post-GPT for large multimodal inference. Saurabh Dighe says if Microsoft were chasing peak performance or trying to span training and inference, Maia would look very different. Higher TDPs. Different tradeoffs. Those paths were pruned early to optimize for one thing: inference price-performance. That focus drives the claim of ~30% better performance per dollar versus the latest hardware in Microsoft’s fleet. Intereting topics include: • What “30% better price-performance” actually means • Who Maia 200 is built for • Why Microsoft bet on inference when designing Maia back in 2022/2023 • Large SRAM + high-capacity HBM • Massive scale-up, no scale-out • On-die NIC integration Maia is a portfolio platform: many internal customers, varied inference profiles, one goal. Lower inference cost at planetary scale. Chapters: (00:00) Introduction (01:00) What Maia 200 is and who it’s for (02:45) Why custom silicon isn’t just a margin play (04:45) Inference as an efficient frontier (06:15) Portfolio thinking and heterogeneous infrastructure (09:00) Designing for LLMs and reasoning models (10:45) Why Maia avoids training workloads (12:00) Betting on inference in 2022–2023, before reasoning models (14:40) Hyperscaler advantage in custom silicon (16:00) Capacity allocation and internal customers (17:45) How third-party customers access Maia (18:30) Software, compilers, and time-to-value (22:30) Measuring success and the Maia 300 roadmap (28:30) What “30% better price-performance” actually means (32:00) Scale-up vs scale-out architecture (35:00) Ethernet and custom transport choices (37:30) On-die NIC integration (40:30) Memory hierarchy: SRAM, HBM, and locality (49:00) Long context and KV cache strategy (51:30) Wrap-up

    53分
  4. Can Pre-GPT AI Accelerators Handle Long Context Workloads?

    1月26日

    Can Pre-GPT AI Accelerators Handle Long Context Workloads?

    OpenAI's partnership with Cerebras and Nvidia's announcement of context memory storage raises a fundamental question: as agentic AI demands long sessions with massive context windows, can SRAM-based accelerators designed before the LLM era keep up—or will they converge with GPUs? Key Takeaways 1. Context is the new bottleneck. As agentic workloads demand long sessions with massive codebases, storing and retrieving KV cache efficiently becomes critical. 2. There's no one-size-fits-all. Sachin Khatti's (OpenAI, ex-Intel) signals a shift toward heterogeneous compute—matching specific accelerators to specific workloads. 3. Cerebras has 44GB of SRAM per wafer — orders of magnitude more than typical chips — but the question remains: where does the KV cache go for long context? 4. Pre-GPT accelerators may converge toward GPUs. If they need to add HBM or external memory for long context, some of their differentiation erodes. 5. Post-GPT accelerators (Etched, MatX) are the ones to watch. Designed specifically for transformer inference, they may solve the KV cache problem from first principles. Chapters   - 00:00 — Intro   - 01:20 — What is context memory storage?   - 03:30 — When Claude runs out of context   - 06:00 — Tokens, attention, and the KV cache explained   - 09:07 — The AI memory hierarchy: HBM → DRAM → SSD → network storage   - 12:53 — Nvidia's G1/G2/G3 tiers and the missing G0 (SRAM)   - 14:35 — Bluefield DPUs and GPU Direct Storage   - 15:53 — Token economics: cache hits vs misses   - 20:03 — OpenAI + Cerebras: 750 megawatts for faster Codex   - 21:29 — Why Cerebras built a wafer-scale engine   - 25:07 — 44GB SRAM and running Llama 70B on four wafers   - 25:55 — Sachin Khatti on heterogeneous compute strategy   - 31:43 — The big question: where does Cerebras store KV cache?   - 34:11 — If SRAM offloads to HBM, does it lose its edge?   - 35:40 — Pre-GPT vs Post-GPT accelerators   - 36:51 — Etched raises $500M at $5B valuation   - 38:48 — Wrap up

    38分
  5. An Interview with Innoviz CEO Omer Keilaf about current LiDAR market dynamics

    1月22日

    An Interview with Innoviz CEO Omer Keilaf about current LiDAR market dynamics

    Innoviz CEO Omer Keilaf believes the LIDAR market is down to its final players—and that Innoviz has already won its seat. In this conversation, we cover the Level 4 gold rush sparked by Waymo, why stalled Level 3 programs are suddenly accelerating, the technical moat that separates L4-grade LIDAR from everything else, how a one-year-old startup won BMW, and why Keilaf thinks his competitors are already out of the race. Omer Keilaf founded Innoviz in 2016. Today it's a publicly traded Tier 1 supplier to BMW, Volkswagen, Daimler Truck, and other global OEMs. Chapters   00:00 Introduction   00:17 Why Start a LIDAR Company in 2016?   01:32 The Personal Story Behind Innoviz   03:12 Transportation Is Still Our Biggest Daily Risk   04:28 The 2012 Spark: Xbox Kinect and 3D Sensing   06:32 From Mobile to Automotive: Finding the Right Platform   07:54 "I Didn't Know What LIDAR Was, But I'd Do It Better"   08:19 How a One-Year-Old Startup Won BMW   10:04 Surviving the First Product   11:23 From Tier 2 to Tier 1: The Volkswagen Win   13:47 Lessons Learned Scaling Through Partners   14:45 The SPAC Decision: A Wake-Up Call from a Competitor   16:42 From 200 LIDAR Companies to a Handful   17:27 NREs: How Tier 1 Status Funds R&D   18:44 Why Automotive-First Is the Right Strategy   19:45 Consolidation Patterns: Cameras, Radars, Airbags   20:31 "The Music Has Stopped"   21:07 Non-Automotive: Underserved Markets   23:51 Working with Secretive OEMs   25:27 The Press Release They Tried to Stop   26:42 CES 2025: 85% of Meetings Were Level 4   27:40 Why Level 3 Programs Are Suddenly Accelerating   28:33 The EV/ADAS Coupling Problem   29:49 Design Is Everything: The Holy Grail Is Behind the Windshield   31:13 The Three-Year RFQ: Grill → Roof → Windshield   32:32 Innoviz3: Small Enough for Behind-the-Windshield   34:40 Innoviz2 for L4, Innoviz3 for Consumer L3   36:38 What's the Real Difference Between L2, L3, and L4 LIDAR?   38:51 The Mud Test: Why L4 Demands 100% Availability   40:50 "We're the Only LIDAR Designed for Level 4"   42:52 Patents and the Maslow Pyramid of Autonomy   44:15 Non-Automotive Markets: Agriculture, Mining, Security   46:15 Closing

    47分

  6. 1月19日

    LiDAR, Explained: How It Works and Why It Matters

    Austin and Vik discuss why LiDAR is important for autonomy, how modern systems work, and how the technology has evolved. They compare Time of Flight and FMCW architectures, explain why wavelength choice matters, and walk through the tradeoffs between 905 nm and 1550 nm across eye safety, cost, and performance. The discussion closes with a clear-eyed look at competition, Chinese suppliers, and supply chain risk. Chapters (00:00) Introduction to LiDAR and why it matters (05:40) The case for LiDAR in autonomous vehicles (12:41) Wavelengths, eye safety, and system tradeoffs (15:38) How LiDAR works: Time of Flight vs. FMCW (20:12) Mechanical vs. solid-state LiDAR designs (27:31) Market dynamics, competition, and geopolitics

    36分

  7. 1月12日

    Nvidia CES 2026

    Episode Summary Austin and Vik break down NVIDIA’s CES 2026 keynote, focusing on Vera Rubin, DGX Spark and DGX Station, uneducated investor panic, and physical AI. Key Takeaways DGX Spark brings server-class NVIDIA architecture to the desktop at low power, aimed at developers, enthusiasts, and enterprises experimenting locally.  DGX Station functions more like a mini-AI rack on-prem: Grace Blackwell for inference and development without full racks The historical parallel is mainframes to minicomputers, expanding compute TAM rather than displacing cloud usage.  On-prem AI converts some GPU rental OpEx into CapEx, appealing to CFOs  NVIDIA positioned autonomy as physical AI with vision-language-action models and early Mercedes-Benz deployments in 2026.  Vera Rubin integrates CPU, GPU, DPU, networking, and photonics into a single platform, emphasizing Ethernet for scale-out. (Where was the Infiniband switch?) The new Vera CPU highlights rising CPU importance for agentic workloads through higher core counts, SMT, and large LPDDR capacity.  Rubin GPU’s move to HBM4 and adaptive precision targets inference efficiency gains and lower cost per token.  Context memory storage elevates SSDs and DPUs, enabling massive KV cache offload beyond HBM and DRAM.  Cable-less rack design and warm-water cooling show NVIDIA’s shift from raw performance toward manufacturability and enterprise polish.

    47分

  8. 1月8日

    Insights from IEDM 2025

    Austin and Vik discuss key insights from the IEDM conference.  They explore the significance of IEDM for engineers and investors, the networking opportunities it offers, and the latest innovations in silicon photonics, complementary FETs, NAND flash memory, and GaN-on-silicon chiplets.  Takeaways Penta-level NAND flash memory could disrupt the SSD marketGaN-on-Silicon chiplets enhance power efficiencyComplementary FETsOptical scale-up has a power problemThe future of transistors is still bright

    42分
  9. Nvidia "Acquires" Groq

    1月5日

    Nvidia "Acquires" Groq

    Key Topics What Nvidia actually bought from Groq and why it is not a traditional acquisitionWhy the deal triggered claims that GPUs and HBM are obsoleteArchitectural trade-offs between GPUs, TPUs, XPUs, and LPUsSRAM vs HBM. Speed, capacity, cost, and supply chain realitiesGroq LPU fundamentals: VLIW, compiler-scheduled execution, determinism, ultra-low latencyWhy LPUs struggle with large models and where they excel insteadPractical use cases for hyper-low-latency inference:Ad copy personalization at search latency budgetsModel routing and agent orchestrationConversational interfaces and real-time translationRobotics and physical AI at the edgePotential applications in AI-RAN and telecom infrastructureMemory as a design spectrum: SRAM-only, SRAM plus DDR, SRAM plus HBMNvidia’s growing portfolio approach to inference hardware rather than one-size-fits-all Core Takeaways GPUs are not dead. HBM is not dead.LPUs solve a different problem: deterministic, ultra-low-latency inference for small models.Large frontier models still require HBM-based systems.Nvidia’s move expands its inference portfolio surface area rather than replacing GPUs.The future of AI infrastructure is workload-specific optimization and TCO-driven deployment.

    41分
  • 9話のエピソード

番組について

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

情報

  • クリエイター
    Vikram Sekar and Austin Lyons
  • 配信期間
    26年
  • エピソード
    9
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © 2026 Semi Doped
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    Semi Doped

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