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VERA RUBIN NVL72 CRUSHES BLACKWELL ON AI INFERENCE

AI DESK1 MIN READ
TUE, SEP 15, 2026

■ AI-SUMMARIZED FROM 1 SOURCE ▸ TIMELINE

AMD's Vera Rubin NVL72 accelerator delivers up to 7x better token throughput per megawatt than Nvidia's Blackwell on large language model inference, according to SemiAnalysis testing. The result exceeds Nvidia CEO Jensen Huang's stated 3x efficiency gains for models in the 1T-3T parameter range.

Testing on a 1.6 trillion-parameter DeepSeek model showed the Vera Rubin NVL72 achieving significantly higher inference efficiency than Blackwell when measured by tokens generated per megawatt of power consumed. SemiAnalysis analyst Bryan Shan attributed the performance gap partly to what he characterizes as conservative claims from Nvidia leadership. The testing suggests AMD's newer architecture offers substantial advantages for inference workloads at scale. The findings carry implications for data center operators evaluating chip purchases for large language model deployment. Per-megawatt efficiency translates directly to operational costs and profitability as AI inference workloads grow. The results underscore ongoing competition between Nvidia and AMD in the AI accelerator market, with efficiency becoming an increasingly important metric alongside raw compute performance.

■ SOURCES

Techmeme

■ SUMMARY WRITTEN BY AI FROM THE LINKS ABOVE

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