AMD covers 5 speculative decoding methods for vLLM on AMD GPUs
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vLLM now supports speculative decoding on AMD GPUs, achieving 1.8–2.4x throughput gains compared to standard autoregressive decoding for models like Gemma-4 and Qwen3.5. This lets teams deploy the same models at significantly lower inference costs or higher request volumes on AMD hardware, with minimal accuracy tradeoffs—critical for production scaling where GPU choice impacts both capex and throughput ceilings.
Speculative decoding in vLLM on AMD GPUs achieves up to 2.59x throughput improvement for certain large language models. This enables production deployments on AMD hardware to process more requests per unit time, directly reducing latency and increasing capacity for LLM-based services. It breaks the previous constraint of lower throughput on non-NVIDIA GPUs, making AMD a more viable option for LLM inference at scale.