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如何让 AI 内存发挥更大作用？深入了解架构，探索 NVIDIA 优化整个系统的 5 种方式，从缩减模型权重到跨服务器复用上下文。
- 谁
- NVIDIA
- 引文
Go inside the architecture to explore 5 ways NVIDIA optimizes the full system, from shrinking model weights to reusing context across servers.
发生了什么
如何让 AI 内存发挥更大作用？ 深入了解架构，探索 NVIDIA 优化整个系统的 5 种方式，从缩减模型权重到跨服务器复用上下文。
接下来读什么
查看这件事的完整进展NVIDIA Outlines Five Ways to Ease AI Inference Memory PressureHow do you make AI memory go further?
Go inside the architecture to explore 5 ways NVIDIA optimizes the full system, from shrinking model weights to reusing context across servers.
这件事到目前为止
NVIDIA outlined five ways its full-stack approach can ease AI inference memory pressure, from shrinking model weights to reusing context across servers. It says NVFP4 reduces model-weight and KV-cache footprints, inactive context can move to lower-cost memory tiers, and retrieval-augmented generation indexes can stay outside high-bandwidth memory (HBM). For SemiAnalysis AgentX, NVIDIA reported up to 10x higher throughput per dollar for GB300 NVL72 than H200 at 100 tokens per second per user, and up to 30x for Vera Rubin NVL72 than GB300 at 125 tokens per second per user.
每天一封，跟上 AI 的重要变化
模型发布、价格变化、算力进展，附原始来源。免费，确认后开始发送，随时一键退订。 看一份往期日报 · 2026-11-05