ggml-webgpu improves prefill speeds for k-quants in llama.cpp PR
Original: ggml-webgpu: Improve prefill speeds for k-quants + refactor matmul for Q4/Q5/Q8 and k-quants by yomaytk · Pull Request #24225 · ggml-org/llama.cpp
A llama.cpp PR boosts ggml-webgpu k-quants prefill throughput by up to 3.78x on M2 Pro tests.
llama.cpp PR #24225 improves ggml-webgpu matrix multiplication performance for k-quants and refactors matmul paths for Q4/Q5/Q8 and k-quants. In pp512 tests on an M2 Pro, reported speedups range from about 1.33x to 3.78x across Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K. The largest gains appear on Q3_K models, including Qwen and Gemma examples.
This r/LocalLLaMA post reposts ggml-org/llama.cpp Pull Request #24225, focusing on improving the prefill speed of ggml-webgpu backends on k-quants format, and refactoring the matrix multiplication implementations of Q4/Q5/Q8 with k-quants. The original focus is not on releasing new models, but on optimizing low-level inference performance: when running quantization models locally or via browser WebGPU paths, the prompt prefill phase often relies heavily on matrix multiplication, so such changes directly affect throughput during long prompts, batch prompts, or context initialization.
Free shows the 3-line summary; Pro unlocks the full deep summary (~300 words) so you never have to click through.
See Pro plans →Want the original English / full article?
Read on r/LocalLLaMA top day →Summaries are AI-generated; the original article is authoritative.