llama.cpp has released version b12023, addressing issue #30482 regarding shared decision prefixes in server-side inference. The update limits shared prefixes to the first required output position of parent and child tasks, preserving decision output embeddings when questions share option tokens. The release includes standard binaries for macOS, Linux, Windows, and Android across CPU, CUDA 12/13, ROCm 10.0, and Vulkan backends. KleidiAI on Apple Silicon is currently disabled in this build.
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llama.cpp Releases · July 13, 2026 · Same story
llama.cpp Releases · October 6, 2026 · Same story
This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
This release quietly expands llama.cpp's hardware support to include Qualcomm Snapdragon devices with CPU, Adreno GPU, and Hexagon NPU acceleration. It also updates ROCm to version 10.0 for both Linux and Windows, keeping AMD users in step with the latest driver stack. CUDA builds now default to 13.4 libraries alongside 12.x options, reflecting the industry's gradual shift toward newer NVIDIA toolkits. While no new model architectures are introduced, the binary matrix grows significantly, ensuring local inference remains viable on a wider array of edge and server hardware.
This release quietly modernizes the local inference stack by adding native support for CUDA 13.4 alongside the existing CUDA 12 builds, ensuring compatibility with the latest NVIDIA driver ecosystems without forcing users to stay on older stacks. The inclusion of ROCm 10.0 and OpenVINO further solidifies llama.cpp as the universal runtime for heterogeneous hardware, while new binaries for Linux arm64 Snapdragon devices bring AI inference to mobile-class ARM silicon. Apple Silicon builds have reverted KleidiAI to disabled by default, a pragmatic choice that prioritizes stability over experimental performance gains for the average user. The result is a more future-proof binary set that removes friction for developers adopting next-generation GPUs and edge processors.
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