Analysis
NVIDIA frames memory management as an economic question as well as a capacity problem. In a November fifth post, NVIDIA argues that adding fast graphics memory helps only if bandwidth can move it. Its account describes a hierarchy where active data stays close and idle context waits in cheaper memory or storage. Model weights and cached context compete for the same fast tier.
NVIDIA says its stack shrinks weights and cached context, distributes weights across as many as seventy-two graphics processors, and moves idle context to lower memory tiers or shared storage. It also describes smaller retrieval embeddings. These are company descriptions of its own system, not independently verified results.
The company reports up to thirty times higher throughput per dollar for Vera Rubin NVL72 than GB300 NVL72 on SemiAnalysis AgentX, with a DeepSeek V4 Pro workload and a target of 125 tokens per second per user. Its post also gives a separate comparison at 100 tokens per second per user. Those workload and response conditions are part of the claim.
The post does not establish results on other models, context lengths, batch sizes or power limits, nor does it establish independent pricing, availability or maintenance needs. If fast memory is your constraint, test on your own workload and target device. Change concurrency and context length one at a time, and track delivery speed alongside cost per useful token.
Source: More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge · 2026-11-05 · Primary source context · attributed account
Why it matters
A memory hierarchy may help under the right workload, but the vendor benchmark cannot substitute for measuring your own cost and response needs.