Models & Labs
llama.cpp b11025 adds CUDA 13 and ROCm 10.0 support
llama.cpp Releaseshigh confidence
Why it matters
- →CUDA 13 and ROCm 10.0 pre-builts eliminate compilation barriers for users with latest-gen NVIDIA and AMD GPUs.
- →OpenEuler Ascend 910b support expands local inference options beyond Western hardware ecosystems.
- →KleidiAI disabled by default suggests stability prioritization over raw performance gains on Apple Silicon.
llama.cpp has released version b11025, introducing pre-built binaries for CUDA 13 and ROCm 10.0 across Linux and Windows platforms. The update also adds support for openEuler with Huawei Ascend 910b ACL Graph acceleration, broadening compatibility with Chinese AI hardware ecosystems. Notably, KleidiAI optimizations on macOS Apple Silicon are disabled by default in this build, while Vulkan and OpenVINO support remain consistent across major operating systems. This release addresses the growing need for support of newer GPU architectures without requiring users to manually compile the library.
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What happened next
- 1llama.cpp b11022 adds CUDA 13 and ROCm 10 support
llama.cpp Releases · September 18, 2026 · Same story
- 2llama.cpp b11050 adds CUDA 13 and ROCm 10.0 support
llama.cpp Releases · September 20, 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.
