Open Source
llama.cpp b9057 Release Expands Platform Support
llama.cpp Releaseshigh confidence
Why it matters
- →Expands support for RISC-V CPUs, enhancing performance options.
- →Increases compatibility with diverse hardware, including Apple Silicon and CUDA.
- →Strengthens llama.cpp's position as a versatile inference runtime.
The b9057 release of llama.cpp introduces optimizations for RISC-V CPUs, enhancing its performance capabilities. This update includes support for a variety of platforms such as macOS, Linux, Windows, and Android, with specific builds for Apple Silicon, Vulkan, and CUDA environments. The addition of ROCm 7.2 for Ubuntu x64 and CUDA 13 for Windows x64 highlights its adaptability to different hardware configurations. This release does not feature new models but strengthens llama.cpp's role as a flexible inference runtime.
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How we got here
- 1llama.cpp b9018 release expands platform support
llama.cpp Releases · May 5, 2026 · Same story
What happened next
- 2llama.cpp b9571 Release Expands Platform Support
llama.cpp Releases · June 9, 2026 · Same story
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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.
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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.
