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llama.cpp b11036 adds CUDA 13 and ROCm 10.0
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Why it matters
- →CUDA 13 support removes compatibility barriers for users with newer NVIDIA drivers.
- →ROCm 10.0 inclusion brings AMD GPU parity with the latest open-source AI stack.
- →Universal runtime status solidified by adding OpenVINO and SYCL accelerators.
llama.cpp has released version b11036, expanding support for modern GPU architectures with CUDA 13 and ROCm 10.0 binaries. The update provides Ubuntu and Windows builds utilizing CUDA 13.3/13.4 libraries and ROCm 10.0, addressing compatibility needs for newer NVIDIA and AMD hardware. Additional builds include OpenVINO, SYCL, and specific openEuler configurations for Ascend chips. Notably, macOS Apple Silicon support is disabled in this release, requiring users to revert to previous versions or wait for the next update.
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llama.cpp b11037 fixes GPU inference bugs
This release prioritizes stability over new features, addressing critical correctness issues in vector handling for GET_ROWS operations. By fixing vec4 alignment checks and updating CUDA libraries to versions 12.8 and 13.3, it ensures reliable performance across NVIDIA hardware on both Linux and Windows. The inclusion of ROCm 10.0 builds further solidifies AMD GPU support without requiring complex configuration. While no new model architectures are added, these fixes prevent silent corruption in local inference tasks that could otherwise go unnoticed.
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llama.cpp b11039 fixes SWA pattern roundtrip
This release finally closes a gap in how llama.cpp handles sliding window attention patterns during model conversion. Previously, loaders silently ignored array-based SWA configurations from models like OLMo2 and Gemma3n, relying on hardcoded defaults that masked potential precision loss. The new Model-Saver now explicitly writes per-layer SWA flags and MLA geometry, ensuring bit-exact roundtrips for a dozen architectures including Plamo3 and Cohere2. This matters because it guarantees that converted GGUF files preserve the exact inference behavior of their original checkpoints, eliminating silent degradation for complex attention mechanisms.
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This is a routine maintenance release for llama.cpp, prioritizing stability over new features. The most notable technical shift is the addition of CUDA 13 builds alongside existing CUDA 12 support, giving users access to newer NVIDIA driver stacks without waiting for major version bumps. ROCm 10.0 support remains available for AMD GPU inference, maintaining parity with previous releases. KleidiAI on Apple Silicon has been explicitly disabled in this build, likely due to stability concerns or testing requirements. There are no new model architectures or quantization methods introduced here.
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