More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge | AI Archive

More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge

nvidia · 2026-11-05T16:00:00+00:00

NVIDIA addresses the growing memory pressure in AI inference through full-stack codesign, which integrates GPUs, CPUs, networking, and software to optimize performance. As models grow larger and agentic applications require more context, GPU high-bandwidth memory (HBM) faces significant strain from model weights and KV cache. Rather than simply increasing HBM capacity, NVIDIA utilizes techniques like NVFP4 precision to shrink data footprints and distribute weights across NVL72 domains. The company also employs NVIDIA Dynamo software to offload inactive context to lower-cost storage tiers while keeping active data in faster memory. This hierarchical approach ensures that expensive HBM is reserved for critical computations, significantly improving efficiency. Consequently, newer platforms like Vera Rubin demonstrate up to a 30x increase in agentic throughput per dollar compared to previous generations, despite only marginal gains in raw HBM capacity. This strategy allows AI factories to deliver more useful tokens within power budgets while reducing costs.