FreeLLMNVIDIA’s full‑stack codesign boosts AI memory efficiency amid rising HBM pressure
The company has introduced full‑stack codesign, integrating GPUs, CPUs, networking, storage and software to use each byte of high‑bandwidth memory (HBM) more ef
The company has introduced full‑stack codesign, integrating GPUs, CPUs, networking, storage and software to use each byte of high‑bandwidth memory (HBM) more efficiently.
Techniques include the NVFP4 4‑bit format for model weights, distributing weights across up to 72 GPUs, and offloading inactive KV‑cache to lower‑cost tiers like NVIDIA CMX storage.
NVIDIA Dynamo inference software moves paused context to CMX, while the Vera CPU’s large LPDDR memory and NVLink‑C2C link provide fast access for resumed context.
あなたにとっての意味
For AI developers, the tighter memory hierarchy means larger models can run with lower latency and cost, especially in multi‑user settings. Users of free or locally hosted models may see slower progress as high‑end memory optimizations stay tied to NVIDIA’s ecosystem, making it harder for open‑source solutions to match the same efficiency without similar hardware support.
ソース
- More Performance From Every Byte: How Full-Stack Codesign Solves AI’s Memory Challenge, NVIDIA (official), 5 Nov 2026
上記のソースから自動的に生成し、当社のコードでチェックしました：すべての数値と名前が含まれ、文がソースをコピーしてはいけません。間違いを見つけましたか？報告する。
今日無料で使えるAIはどれですか？
すべての無料AIモデルをブラインドヒューマン投票で順位付けし、あなたのGPUで必要なメモリと速度を示します。