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llama.cpp b11110 adds ROCm 10 and Snapdragon support
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Why it matters
- →ROCm 10.0 support removes the last major barrier for AMD GPU users running local LLMs.
- →Snapdragon Linux binaries position llama.cpp as a key runtime for ARM-based AI laptops.
- →CUDA 13.4 inclusion future-proofs builds against upcoming NVIDIA driver requirements.
llama.cpp has released version b11110, expanding hardware compatibility for local AI inference. The update adds ROCm 10.0 support for AMD GPUs on Linux and Windows, alongside new binaries for Linux arm64 targeting Snapdragon devices with CPU, Adreno GPU, and Hexagon NPU acceleration. CUDA builds now include libraries for versions 12.8 and 13.4 across multiple architectures. macOS KleidiAI support is currently disabled in this build.
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llama.cpp b11104 adds multi-addr server binding
The llama-server now binds to multiple addresses, a practical upgrade for anyone running local inference behind reverse proxies or complex network setups. This change removes the previous single-address limitation, allowing flexible routing without external workarounds. While the release includes standard binaries for CUDA 13 and ROCm 10.0, the networking feature is the real differentiator here. It makes self-hosted deployments slightly more robust for power users who need granular control over traffic flow.
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llama.cpp b11105 adds ROCm 10 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally bringing full ROCm 10.0 support to both Linux and Windows, closing a long-standing gap for AMD GPU users who previously had to rely on workarounds or older versions. The inclusion of CUDA 13 builds alongside CUDA 12 ensures compatibility with the latest NVIDIA driver stacks without forcing users into beta territory. Perhaps most notably, the addition of native Snapdragon support on Linux marks a significant step toward efficient AI inference on ARM-based mobile and edge devices, expanding the hardware ecosystem beyond traditional x86 and NVIDIA dominance.
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