Nvidia "Acquires" Groq
Impossibile aggiungere al carrello
Puoi avere soltanto 50 titoli nel carrello per il checkout.
Riprova più tardi
Riprova più tardi
Rimozione dalla Lista desideri non riuscita.
Riprova più tardi
Non è stato possibile aggiungere il titolo alla Libreria
Per favore riprova
Non è stato possibile seguire il Podcast
Per favore riprova
Esecuzione del comando Non seguire più non riuscita
Nvidia "Acquires" Groq
-
Letto da:
-
Di:
A proposito di questo titolo
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.
Ancora nessuna recensione