AI BriefingvLLM Adds HIP W4A16 Kernel Support for AMD
Key point
A native HIP W4A16 kernel that optimizes performance on AMD GPUs has been added to vLLM.
Details
A native HIP W4A16 kernel that optimizes inference performance for 4-bit quantized models on AMD GPU (ROCm) environments has been merged into the vLLM project.
With this update, inference speed for W4A16 quantized models on RDNA3-architecture GPUs has significantly improved. Key benchmark results based on the Qwen3.6-27B-GPTQ-W4A16-G32 model are as follows:
- RDNA3 W4A16 (new kernel) bf16: 205.3 tk/s
- RDNA3 W4A16 (new kernel) fp16: 270.2 tk/s
With improved performance over the existing Triton approach, this is expected to offer greater efficiency for developers looking to serve LLMs using AMD hardware.
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