NVIDIA 如何通过协同设计最大化 AI 工厂中每个字节的价值
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
NVIDIA 提出五种协同设计方法，以最大化 AI 工厂中 HBM 每个字节的有效产出。其核心思路是把冗余权重副本、闲置会话的 KV cache 和重复计算的前缀视为浪费，衡量标准从 GPU 内存容量转向每字节能产出多少 token。
In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent on a redundant weight copy, an...
In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent on a redundant weight copy, an idle session’s KV cache, or a recomputed prefix is a byte that is not being used to produce tokens. The question is no longer how much memory a GPU has, but how much useful work each byte delivers. The generational data in Figure 1 below…
来源：NVIDIA Technical Blog · developer.nvidia.com