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2026-10-26

llama.cpp releases00:30

b11880

tools : reject truncated GGUF tensor data in merge (#31044) Co-authored-by: Shreya Agrawal **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/lla

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llama.cpp releases00:12

b11879

CUDA: fuse FFN gate/up matmuls and GLU in MMQ with interleaved gate/up rows (#29948) * cuda: fuse FFN gate/up and GLU in MMQ with interleaved gate/up rows For prompt processing, run the gate and up matmuls of a dense FFN as one mul_mat_q launch and apply the GLU in the write-back. - The loaders take the gate weights as a second pointer and fill the weight tile with up and gate rows interleaved in chunks of 8, so each thread holds an up value and the matching gate value (rows r and r + 8 of an MMA tile). - The MMA loop is unchanged: same tile, warp layout, vec_dot and one sum[] per thread. The fused write-back computes the GLU from registers and writes half the rows. - The fused path uses the regular tiling (no stream-k) with the column tiles innermost so that neighbouring blocks share weig

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2026-10-25

llama.cpp releases23:16

b11878

ggml-cuda: fuse qwen MRoPE + set-rows (#31025) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878

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llama.cpp releases22:57

b11877

vulkan: Add a larger tile size for conv3d (coopmat2 only) (#29920) * vulkan: Add a larger tile size for conv3d (coopmat2 only) This adds a larger tile size, in practice it is only for coopmat2 where we don't need shared memory to store the output (would use more than 64KB). Add some additional representative backend tests. At least, this is what codex came up with from looking at sd.cpp Wan VAE. * only apply large tile size for newer driver versions **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11877/llama-b11877-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama

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llama.cpp releases14:28

llama.cpp releases version b11871: adds a limit on JSON nesting depth to prevent service crashes

llama.cpp introduced a critical security fix in version b11871, imposing a hard limit of 128 nested JSON levels on requests to prevent HTTP thread stack overflows and service crashes caused by deep recursive parsing (triggering SIGBUS on macOS and SIGSEGV on Linux). This policy covers multiple input scenarios for the chat interface and uniformly returns an HTTP 400 error.

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llama.cpp releases10:15

llama.cpp has released version b11870, officially supporting the DeepSeek-V4.1-Flash model.

llama.cpp has added support for the DeepSeek-V4.1-Flash (deepseek41) model in version b11870, and implemented several low-level optimizations tailored to its Engram architecture, including simplification of KV cache computation, replacement of MXFP8 with BF16 precision, and in-graph row gather operations; it also provides multi-platform binary builds covering macOS, Linux, Windows, Android, iOS, and openEuler.

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llama.cpp releases05:18

llama.cpp releases version b11869: fixes Jinja template function argument handling and adds multi-platform binary support.

The llama.cpp project has released build version b11869, which primarily fixes the parameter-parsing logic for the select and selectattr functions in Jinja templates and provides precompiled binaries that support dozens of hardware–backend combinations across macOS, Linux, Windows, Android, iOS, and openEuler.

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llama.cpp releases04:23

llama.cpp releases version b11868: enhanced Hexagon NPU multi-core Flash Attention support, with cross-platform compilation and build capabilities.

llama.cpp v2.45.0 (tag b11868) has been officially released, with the core update being the introduction of a KV-head splitting mechanism in the Hexagon backend to optimize multi-core HMX Flash Attention performance and unify the FA splitting control flag. Binary packages are simultaneously available for all major platforms, including macOS, Linux, Windows, Android, iOS, and openEuler, supporting multiple backends such as CUDA 12/13, Vulkan, ROCm, OpenVINO, SYCL, and Snapdragon (Adreno + Hexagon).

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llama.cpp releases04:11

llama.cpp releases version b11867: server-side entry point refactored, with full support for multiple platforms and heterogeneous acceleration backends.

llama.cpp has officially released version b11867 on GitHub. Key changes include a refactored server entry point (PR #31113) and the addition of the server_role mechanism. Additionally, more than 40 precompiled binaries are now available, covering macOS, Linux, Windows, Android, iOS, and openEuler, with support for diverse hardware backends such as CPU, CUDA (12.4/12.8/13.4), Vulkan, ROCm 10.0, OpenVINO, SYCL (FP16/FP32), and Snapdragon (Adreno + Hexagon).

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llama.cpp releases03:51

llama.cpp releases version b11866: removes redundant AMAX scaling logic from the Metal backend and streamlines the MoE computation path.

llama.cpp v.b11866 has removed the outdated src1 power-of-two AMAX scaling mechanism from the Metal backend, instead relying on a more granular per-expert-token amax scaling within the computation graph, ensuring that MoE projection activation values remain within the f16 representation range; concurrently, it also deleted the associated two-stage reduction kernels, F32-precision scheduling, and out-of-bounds test cases.

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llama.cpp releases03:30

llama.cpp releases version b11865: adds RDMA receive boundary checking and supports multiple platform backends, including CUDA 13.4, ROCm 10.0, and Snapdragon NPU.

The llama.cpp project has released build version b11865, with core changes including the addition of memory-boundary checks in the RDMA receive logic (PR #30399) and a significant expansion of hardware-acceleration support, covering CUDA 13.4, ROCm 10.0, Vulkan, OpenVINO, SYCL, and Qualcomm Snapdragon—providing full-stack heterogeneous computing backends for CPU, Adreno, and Hexagon. macOS KleidiAI support and openEuler builds have been explicitly marked as DISABLED.

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llama.cpp releases03:13

llama.cpp releases version b11861: fixes an inference exception caused by the minimax-m3 model’s quantized scaling tensor not being propagated.

llama.cpp v.b11861 fixes the weight distortion issue in the minimax-m3 model that occurred under NVFP4/MXFP8 quantization due to the scale tensor not being injected into the computation graph, thereby preventing “token soup” and residual vanishing; it also provides multi-platform binary builds covering macOS, Linux, Windows, Android, iOS, and openEuler.

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