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AI CURATED HOT STORY · 11/6/2026

Nvidia Claims 30x Agent Throughput per Dollar with Only 3% More HBM

1 reports archived1 independent sourcesupdated 11/6/2026, 07:30:59
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Nvidia published a technical explainer arguing that a full-stack co-design approach can ease the HBM crunch and memory wall. Its stack combines NVFP4 low-precision compression, weight sharding across up to 72 GPUs in NVL72, LPDDR buffering on Vera CPUs, Dynamo context migration and CMX context storage to move inactive context off HBM. Citing the SemiAnalysis AgentX benchmark, Nvidia claims Vera Rubin NVL72 delivers up to 30x agent throughput per dollar versus GB300 NVL72 while HBM capacity per GPU grows only about 3%, signaling a shift from raw memory capacity to effective tokens per byte and per watt.

LATEST/Nvidia published a technical explainer arguing that a full-stack co-design approach can ease the HBM crunch and memory wall. Its stack combines NVFP4 low-precision compression, weight sharding across up to 72 GPUs in NVL72, LPDDR buffering on Vera CPUs, Dynamo context migration and CMX context storage to move inactive context off HBM. Citing the SemiAnalysis AgentX benchmark, Nvidia claims Vera Rubin NVL72 delivers up to 30x agent throughput per dollar versus GB300 NVL72 while HBM capacity per GPU grows only about 3%, signaling a shift from raw memory capacity to effective tokens per byte and per watt.

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Total 1 reports · Latest first
  1. IT之家 · 智能时代T2·78 pts

    Nvidia Claims 30x Agent Throughput per Dollar with Only 3% More HBM

    • The core idea is tiering model weights, KV cache and RAG indexes to cut HBM occupancy via compression and migration
    • The stack combines NVFP4 compression, NVL72 weight sharding, Vera CPU LPDDR buffering and Dynamo/CMX context storage
    • The cited benchmark claims up to 30x agent throughput per dollar with only ~3% more HBM, based on a vendor-cited third-party benchmark