Models & Labs
llama.cpp b10982 Release Adds Vulkan Support
llama.cpp Releaseshigh confidence
Why it matters
- →Adds support for sparse Flash Attention on Vulkan, enhancing performance.
- →Improves reliability by avoiding nondeterministic atomic operations.
- →Expands platform compatibility, offering more options for developers.
The b10982 release of llama.cpp brings significant updates, including support for sparse Flash Attention on Vulkan, specifically for DSV4/GLM. This release focuses on improving implementation tuning, adding tests, and ensuring consistent variable naming. It also addresses potential issues with nondeterministic atomic operations. The update broadens platform compatibility, supporting macOS, Linux, Windows, and openEuler, making it a robust option for developers across different systems.
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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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