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 market
• GaN-on-Silicon chiplets enhance power efficiency
• Complementary FETs
• Optical scale-up has a power problem
• The future of transistors is still bright
  
  

Key Topics

• What Nvidia actually bought from Groq and why it is not a traditional acquisition
• Why the deal triggered claims that GPUs and HBM are obsolete
• Architectural trade-offs between GPUs, TPUs, XPUs, and LPUs
• SRAM vs HBM. Speed, capacity, cost, and supply chain realities
• Groq LPU fundamentals: VLIW, compiler-scheduled execution, determinism, ultra-low latency
• Why LPUs struggle with large models and where they excel instead
• Practical use cases for hyper-low-latency inference:
  • Ad copy personalization at search latency budgets
  • Model routing and agent orchestration
  • Conversational interfaces and real-time translation
  • Robotics and physical AI at the edge
  • Potential applications in AI-RAN and telecom infrastructure
• Memory as a design spectrum: SRAM-only, SRAM plus DDR, SRAM plus HBM
• Nvidia’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.