Models & Labs
llama.cpp b10010 release adds CORS options
llama.cpp Releaseshigh confidence
Why it matters
- →New CORS options enhance web application integration for developers.
- →Expanded platform support increases deployment flexibility.
- →The update focuses on improving server-side capabilities without new model changes.
The llama.cpp project has released version b10010, which includes new server options for Cross-Origin Resource Sharing (CORS). This update allows developers to specify CORS settings, including a special 'localhost' value, enhancing web application integration. The release also adds tests to ensure the robustness of these new features. While no new model architectures are introduced, the update broadens platform support, making llama.cpp more versatile for developers across different systems.
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How we got here
- 1llama.cpp b9753 Release Fixes and Enhancements
llama.cpp Releases · June 22, 2026 · Same story
- 2Llama.cpp b9927 Release Moves to HTTP Implementation
llama.cpp Releases · July 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.
llama.cpp Releases
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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.
