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llama.cpp b11093 adds CUDA 13 and ROCm 10.0
llama.cpp Releaseshigh confidence
Why it matters
- →Users with newer NVIDIA GPUs requiring CUDA 13 can now run pre-built binaries without compiling.
- →AMD ROCm 10.0 support is added, improving accessibility for non-NVIDIA hardware accelerators.
- →Simultaneous CUDA 12 and 13 builds allow flexible deployment across different driver versions.
llama.cpp has released version b11093, expanding its binary distribution to include native support for CUDA 13 and ROCm 10.0. The update provides pre-built binaries for Ubuntu and Windows, allowing users with newer NVIDIA GPUs (requiring CUDA 13) and AMD accelerators (using ROCm 10) to run models without manual compilation. Existing builds for CUDA 12, Vulkan, OpenVINO, and SYCL remain available, ensuring backward compatibility. This release significantly broadens the hardware compatibility of the popular local inference engine.
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This update quietly extends llama.cpp’s hardware support to ROCm 10.0 and CUDA 13.4 across Linux and Windows, keeping the library competitive as NVIDIA pushes newer driver stacks. The test suite also gains regex filtering for backend operations, a practical improvement for developers debugging specific inference paths. While no new model architectures are introduced, this release ensures compatibility with the latest GPU ecosystems without forcing users to wait for major version bumps.
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llama.cpp b11081 release with CUDA 13 and ROCm 10 support
This release quietly cements llama.cpp as the universal inference runtime by adding default builds for CUDA 13.4 and ROCm 10.0, effectively closing the gap on newer NVIDIA and AMD hardware without requiring manual compilation flags. The inclusion of KleidiAI for Apple Silicon remains a key differentiator for local Mac users seeking optimized ARM kernels. While the changelog details internal test improvements like configurable tensor standard deviation, the real value lies in the expanded binary matrix that supports developers across the latest GPU architectures. Readers can now deploy on cutting-edge hardware out of the box. The update ensures that teams using the newest graphics cards do not need to wait for source code patches to achieve stable performance. This is a practical step toward making local inference truly hardware-agnostic.
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