NVIDIA Launches Full-Stack Codesign to Ease AI Inference Memory Pressure
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NVIDIA has introduced a full-stack codesign solution to ease GPU memory pressure during AI inference. The approach compresses model weights and KV cache with NVFP4 precision, distributes weights within NVL72 domains, offloads inactive context to Vera CPU and the CMX capacity layer, and places RAG indexes outside HBM. On NVIDIA's own SemiAnalysis AgentX DeepSeek V4 Pro workload, the company says GB300 NVL72 achieves up to 10 times the per-dollar throughput of H200, at 100 tokens per user per second. NVIDIA cautions that this result is tied to that specific workload and platform comparison.
Written with AI from the reporting · updated 11/5/2026
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- 1Unclassified fact stationNov 6, 01:00single sourcereport-time only
NVIDIA 发布全栈协同设计方案以缓解 AI 推理内存压力
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- Subject：
- nvidia
- Action：
- 发布
- Object：
- 全栈协同设计解决 AI 内存挑战
- Occurred (fact side)：
- not recorded
- Published (valid side)：
- Nov 6, 00:00
- First seen (known side)：
- Nov 6, 01:00
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- NVIDIA BlogSelectedFirst-party sourceRelated fact: single sourceFact occurrence time unverifiedNVIDIA Unveils Full-Stack Codesign to Boost AI Inference Memory Efficiency
Report first recorded Nov 6, 01:00
NVIDIA's official blog explains how full-stack co-design across GPUs, CPUs, networking, storage and software reduces AI inference memory pressure. Using NVFP4, NVL72 weight distribution, Vera CPU offloading and CMX context storage, the company says HBM capacity per GPU rose only about 3% from Blackwell Ultra to Vera Rubin, while agentic throughput per dollar increased up to 30x on SemiAnalysis AgentX.
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