vLLM Achieves Major Throughput Gains on AMD GPUs
vLLM Blog · rss · 2026-07-13
A vLLM blog post details how to train, quantize, and deploy an EAGLE-3 speculative decoding draft model on AMD Instinct GPUs using vLLM + AMD Quark.
The core result is a significant boost in inference throughput, with reported figures including:
- Kimi-K2.5: Up to 2.00x throughput increase
- MiniMax-M2.5: Up to 1.79x throughput increase
The article focuses less on the models themselves and more on combining speculative decoding, quantization, and the serving stack to achieve higher serving efficiency on AMD hardware.
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