AI Briefingllama.cpp Improves Inference Speed with CUDA-based FWHT
·2026.05.26 02:22
Key point
A CUDA-based FWHT has been added to llama.cpp, improving inference speed when using KV-cache quantization.
Details
An update implementing the Fast Walsh-Hadamard Transform (FWHT) in CUDA has been proposed for llama.cpp. This feature improves computational efficiency when using KV-cache quantization.
Testing on an RTX 5090 environment showed the following performance improvements:
- Prefill (pp): approximately 1~2% performance improvement
- Token Generation (tg): approximately 7~9% performance improvement
This optimization is particularly effective in configurations that use quantized KV-cache, such as -ctk q8_0 -ctv q8_0.
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