Open Source
llama.cpp b10502 Release Expands Platform Support
llama.cpp Releaseshigh confidence
Why it matters
- →Expands platform compatibility, enhancing versatility for developers.
- →Disabling certain features suggests a focus on stability and refinement.
- →Continues to position llama.cpp as a flexible inference runtime.
The b10502 release of llama.cpp has been announced, featuring expanded support across various platforms. Notably, the release includes compatibility for Windows with CUDA 13 and Vulkan, but disables support for macOS Apple Silicon with KleidiAI and Ubuntu x64 with ROCm 7.14. This update highlights llama.cpp's ongoing efforts to enhance its versatility as an inference runtime across different hardware. Despite some disabled features, the release maintains a broad platform reach, emphasizing stability and refinement over new feature introductions.
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How we got here
- 1llama.cpp b10012 release expands platform support
llama.cpp Releases · July 15, 2026 · Same story
- 2llama.cpp b10064 Release Expands Platform Support
llama.cpp Releases · July 19, 2026 · Same story
More from llama.cpp Releases
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llama.cpp b12022 adds ROCm 10 and Snapdragon support
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.
llama.cpp Releases
Coding Toolscoding
llama.cpp b12023 fixes shared prefix bug
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.
