Models & Labs
Llama.cpp b10981 release enhances OpenVINO support
llama.cpp Releaseshigh confidence
Why it matters
- →Enhances OpenVINO integration, improving model performance and reliability.
- →Introduces new features for better handling of complex AI models.
- →Increases versatility for developers working with diverse hardware setups.
Llama.cpp's b10981 release focuses on enhancing OpenVINO support, optimizing stateful decode and GPU MoE inference. Key improvements include fixing stateful decode errors and refining sliding-window layer handling, which boosts model reliability and performance. The update also introduces features like GGML_OPENVINO_REQUANT_KQUANT for 4-bit requantization and better multi-head model support. These enhancements make llama.cpp more adaptable for developers using complex AI models across various hardware platforms.
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llama.cpp b10993 adds ROCm 10 and CUDA 13 builds
This release quietly extends llama.cpp's hardware support to the latest driver stacks, adding official binaries for ROCm 10.0 and CUDA 13 across Linux and Windows. For AMD users, this means native compatibility with newer GPU architectures without manual compilation tweaks, while NVIDIA users gain access to the latest CUDA runtime optimizations. The inclusion of WebGPU in CI signals ongoing work toward browser-based inference, though it remains a background effort for now. There are no new model formats or quantization methods here, just broader infrastructure coverage that keeps llama.cpp relevant as hardware evolves.
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llama.cpp fixes Metal NaN bug for large activations
A critical precision bug in llama.cpp’s Apple Silicon backend has been patched, resolving total inference failures on models with high-activation ranges like Mistral Small 4. The issue stemmed from f16 saturation during matrix multiplication, which turned entire output tensors into NaN values for inputs exceeding ~32 tokens. By implementing an exact, power-of-two rescaling mechanism in the Metal kernel, the fix restores correctness without significant performance penalties. This ensures local inference on M-series chips remains viable for complex MoE architectures that previously crashed.
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