NVIDIA details memory co-design and agent throughput cost optimization strategies

On Thursday, November 5, 2026 (US Eastern Time), NVIDIA published a company blog post giving a detailed explanation of its holistic AI memory stack co-design strategy. The post argues that as large model parameter counts, context lengths, and Agent deployments keep expanding, memory pressure on AI infrastructure is intensifying, and simply adding more GPU memory capacity is no longer a sufficient answer.

NVIDIA proposes treating memory and storage in the AI factory as a single data hierarchy rather than pushing as much data as possible into the fastest and most expensive GPU memory. Instead, the company says, the entire stack should be co-designed: GPU, CPU, networking, storage, and software, combined with techniques such as model weight quantization, KV Cache compression and tiered storage, and retrieval data optimization improve overall memory efficiency. The strategic goal is to process more AI work per unit cost.

In the post, NVIDIA disclosed that from the Blackwell Ultra to the Vera Rubin platform, per-GPU HBM capacity increases by only about 3%, yet on specific Agent workloads the throughput per dollar can rise up to 30x. The company presents this as evidence that higher per-unit-cost efficiency can be achieved without proportionally larger memory budgets.

Notably, the same day the technical post went live, NVIDIA shares were sold off heavily. During Thursday's session the stock fell more than 9% at one point, wiping out its year-to-date gains and turning negative, with market value evaporating by nearly $1.4 trillion from the record high set in October. The episode currently stands at the stage of a sharp same-day share drop and market debate over AI infrastructure spending, with no further company response reported.