Pre-release / continuous build: this is an experimental build, not a stable release.
metal : few-row MMA mat-mul for the remaining src0 types (#30065) The generic few-row MMA kernel works for any type with a 16-weight dequantizer, so it now also takes BF16, Q1_0, Q2_0, MXFP4, Q2_K, Q3_K, TQ2_0 and the IQ types. Each type starts at the row count where it beats the current kernels on an M3 Ultra: 5 rows for TQ2_0, 4 for BF16, 3 for MXFP4, Q2_0, Q2_K and IQ4_NL, and 2 for the others. test-backend-ops perf -o MUL_MAT, m=4096, k=14336, M3 Ultra, time of this change over master (mean of two interleaved runs each): 0.23 to 0.98 from the threshold to 8 rows, 0.24 to 0.33 at 9 to 16 rows, and 0.99 to 1.01 at 1 and 512 rows.Website: - https://llama.app
Attestations: - https://github.com/ggml-org/llama.cpp/attestations/53605242
macOS/iOS: - macOS Apple Silicon (arm64) - macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED - macOS Intel (x64) - iOS XCFramework
Linux: - Ubuntu x64 (CPU) - Ubuntu arm64 (CPU) - Ubuntu s390x (CPU) - Ubuntu x64 (Vulkan) - Ubuntu arm64 (Vulkan) - Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries - Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries - Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries - Ubuntu x64 (ROCm 10.0) - Ubuntu x64 (OpenVINO) - Ubuntu x64 (SYCL FP32) - Ubuntu x64 (SYCL FP16) - Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide
Android: - Android arm64 (CPU) - Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide
Windows: - Windows x64 (CPU) - Windows arm64 (CPU) - Windows arm64 (OpenCL Adreno) - Windows x64 (CUDA 12) - CUDA 12.4 DLLs - Windows x64 (CUDA 13) - CUDA 13.4 DLLs - Windows arm64 (CUDA 13) - CUDA 13.4 DLLs - Windows x64 (Vulkan) - Windows arm64 (Vulkan) - Windows x64 (OpenVINO) - Windows x64 (SYCL) - Windows x64 (ROCm 10.0)
openEuler: - DISABLED - openEuler x86 (310p) - openEuler x86 (910b, ACL Graph) - openEuler aarch64 (310p) - openEuler aarch64 (910b, ACL Graph)
UI: - UI