Nvidia's vLLM Optimizations Outperform AMD on MoE Models, Reaching 12000+ Tokens Per Second

NVDA-3.53%
AMD-4.22%
According to SemiAnalysis on July 13, Nvidia demonstrated clear performance advantages in vLLM inference optimization over AMD, with the GB200 NVL72 architecture reaching over 12,000 tokens per second throughput on mixture-of-experts models like Kimi K2.5. The analysis highlighted that Nvidia's Dynamo distributed inference framework, deeply integrated with vLLM, enables efficient expert parallelization and KV cache optimization, while AMD's MI355X currently relies on standard vLLM versions without comparable depth optimization for large-scale MoE scenarios.
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