Why LPUs struggle with large models

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.