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llama.cpp b11115 adds AMD ROCm 10 and Snapdragon support
llama.cpp Releaseshigh confidence
Why it matters
- →ROCm 10.0 support allows AMD GPU users to run local inference without compiling from source.
- →Snapdragon arm64 builds enable direct testing on emerging AI PC hardware running Linux.
- →The release maintains broad compatibility with CUDA 12/13 and Vulkan for existing NVIDIA and GPU users.
llama.cpp has released version b11115, expanding its pre-built binary support to include ROCm 10.0 and Linux arm64 Snapdragon platforms. The update adds OpenCL binary kernels for specific quantization formats and renames selection helpers for better code clarity. Notably, KleidiAI acceleration on macOS Apple Silicon is currently disabled in this build. The release maintains existing support for CUDA 12/13, Vulkan, and various CPU architectures across Windows, Linux, and Android.
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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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