github

<details open> llama-bench : init params.offline (#25476) Signed-off-by: Adrien Gallouët <angt@huggingface.co> </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9940/llama-b9940-bin-macos-arm64.tar.gz) - macOS A

<details open> metal : add CONV_2D_DW (depthwise convolution) support (#21565) * metal : add CONV_2D_DW (depthwise 2D convolution) support * test : add perf cases for CONV_2D_DW * metal : use 3D dispatch for CONV_2D_DW kernel * metal : add channel-tiled CONV_2D_DW kernel for

<details open> ggml-hip: enable -funsafe-math-optimizations (#24668) CUDA is compiled with fast math and AMD/HIP is not — this flag lets AMD use fast math too. We can't use -ffast-math: it implies -ffinite-math-only, which won't compile (ggml uses INFINITY for masking) and pro

<details open> cuda: align snake fusion matcher with the other backends (#25460) * cuda: fix snake fusion type predicate, a and inv_b are F32 The matcher required a->type == x->type while launch_snake reads both as const float *, matching the CPU and Metal contract where a and

<details open> server : respect min-step when splitting prompt batches (#25420) </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9936/llama-b9936-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI en

<details open> hexagon: add VISION RoPE support (#25216) * hexagon: add VISION RoPE support * hexagon: support RoPE on strided half-dim views for all modes * hex-rope: decouple src0 DMA copy size from row stride * hex-rope: support non-contiguous dst for RoPE * hex-rope: fi

<details open> ggml-webgpu: tune subgroup split (d_split) in flash_attn_vec (#25418) </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9934/llama-b9934-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, Kleidi

<details open> opencl: Q6_K GEMM/GEMV fix for ne01 of weights that are not multiples of 128. (#25464) * opencl: fix garbled output for Q6_K weights with ne01 % 128 != 0 on Adreno Observed with granite-3.1-3b-a800m-instruct, whose vocab is an odd number. Route Q6_K dense mul

<details open> vulkan: disable FA mask_opt on GCN to improve performance (#24362) * vulkan: disable FA mask_opt on GCN to improve performance * reenable mask opt over attention head size 256 </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-or

<details open> opencl: ragged-tile MoE prefill FP16 GEMM optimization (skip padded expert tiles) (#25433) * opencl: ragged-tile MoE prefill GEMM (skip padded expert tiles) The MoE prefill GEMM groups tokens into TILESIZE_N=32 per-expert tiles; at low tokens-per-expert most til

<details open> llama-batch: fix allowed decreasing pos in a seq (#25449) </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9930/llama-b9930-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled)

<details open> vulkan: for small AMD GPUs, reduce submission threshold based on CU count (#25240) </details> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b9929/llama-b9929-bin-macos-arm64.tar.gz) - macOS Apple Silicon (

<details open> hexagon: new vtcm layouts and improved pipelines for MUL_MAT, MUL_MAT_ID and FLASH_ATTN_EXT (#25425) * hex-fa: refactor kernel param compute to use common layout builder * hmx: add explicit compiler barriers to make hmx funcs more robust * hex-vtcm: more generi