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.
