NVIDIA Touts Full-Stack Codesign to Ease AI Inference Memory Pressure

NVIDIA claims platform-wide codesign delivers sharply better inference efficiency as HBM demand grows.

IT Solution Journal News Desk

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NVIDIA says rising model sizes, longer contexts, and agentic workloads are intensifying GPU memory pressure as supply tightens. The company describes a full-stack codesign strategy spanning GPUs, CPUs, networking, storage, and software to extract more work from each memory tier. Vendor-cited benchmarks from SemiAnalysis AgentX attribute up to 30x higher agentic throughput per dollar on Vera Rubin NVL72 versus GB300 NVL72 at 125 tokens per second per user. Supporting techniques include NVFP4 weight and KV-cache compression, context offload to NVIDIA CMX storage via Dynamo software, and CPU-memory buffering using the Vera CPU over NVLink-C2C. NVIDIA CMX partners listed include Dell, HPE, IBM, NetApp, and others.

Source: NVIDIA News. Original announcement: November 05, 2026.