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llama.cpp b12032 adds CUDA 13 and Snapdragon support
llama.cpp Releaseshigh confidence
Why it matters
- →CUDA 13 support future-proofs local inference for users upgrading to the latest NVIDIA driver stacks.
- →Official Snapdragon arm64 binaries enable efficient LLM running on high-performance ARM laptops and devices.
- →ROCm 10.0 and openEuler ACL Graph updates broaden hardware options beyond NVIDIA for enterprise and specialized AI hardware.
llama.cpp has released version b12032, expanding its binary support to include CUDA 13 (versions 12.8 and 13.4) and ROCm 10.0 across Linux and Windows platforms. A significant addition is the first official Linux arm64 build for Qualcomm Snapdragon devices, enabling CPU, Adreno GPU, and Hexagon NPU acceleration. The release also adds support for openEuler with ACL Graph for Huawei Ascend chips (310p and 910b). Notably, KleidiAI on Apple Silicon is disabled in this build, while Vulkan and OpenVINO support continues to expand across x64 and arm64 architectures.
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Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
- 1llama.cpp b11998 adds CUDA 13 and Snapdragon support
llama.cpp Releases · October 30, 2026 · Same story
What happened next
- 2llama.cpp b12029 adds CUDA 13 and Snapdragon support
llama.cpp Releases · November 1, 2026 · Same story
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This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
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This release quietly expands llama.cpp's hardware support to include Qualcomm Snapdragon devices with CPU, Adreno GPU, and Hexagon NPU acceleration. It also updates ROCm to version 10.0 for both Linux and Windows, keeping AMD users in step with the latest driver stack. CUDA builds now default to 13.4 libraries alongside 12.x options, reflecting the industry's gradual shift toward newer NVIDIA toolkits. While no new model architectures are introduced, the binary matrix grows significantly, ensuring local inference remains viable on a wider array of edge and server hardware.
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