llama.cpp has released version b11563, adding support for CUDA 13.4 and ROCm 10.0 binaries across Linux and Windows platforms. The update also changes default symbol visibility to hidden, preventing internal GGML symbols from leaking via the public API. Notably, KleidiAI builds for Apple Silicon are currently disabled in this release. The package continues to support a wide range of hardware including Vulkan, OpenVINO, and various CPU architectures.
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llama.cpp Releases · September 28, 2026 · Same story
llama.cpp Releases · September 30, 2026 · Same story
This release quietly cements llama.cpp as the universal inference runtime by finally bringing first-class ROCm 10.0 support to both Linux and Windows. AMD GPU users no longer need workarounds, effectively closing a long-standing parity gap with NVIDIA's CUDA ecosystem. The inclusion of Snapdragon AI stack binaries for Linux marks a significant expansion into mobile-edge hardware, while the addition of CUDA 13 builds future-proofs the tool against upcoming driver requirements. By standardizing these complex backend integrations into single downloadable binaries, the project removes friction for anyone deploying local models on non-NVIDIA silicon.
Prism's Bonsai 2 27B model is now natively supported in llama.cpp, bringing a high-efficiency architecture to the local inference ecosystem. The release includes specific runtime optimizations for its Hadamard transformations, ensuring smooth execution across diverse hardware backends like CUDA and ROCm. This integration allows developers to run a specialized 27B parameter model without custom conversion scripts or external wrappers. It expands the practical toolkit for those needing compact yet capable models on consumer-grade GPUs.
This release refactors llama.cpp’s RPC backend logging, turning a binary debug flag into a granular verbosity system. Developers can now trace tensor transfers and graph cache evictions with precision, which is critical for debugging distributed inference setups. The cleanup also unifies log handling across client and server sides, reducing noise and fixing format specifier bugs. It’s a maintenance win that makes diagnosing remote inference failures significantly easier.
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