Models & Labs
llama.cpp b11680 Hexagon NPU and CUDA 13 support
llama.cpp Releaseshigh confidence
Why it matters
- →Hexagon NPU performance is significantly improved with proper tiling and vectorization for mobile inference.
- →CUDA 13 support allows users on the latest NVIDIA drivers to run llama.cpp without compatibility issues.
- →Fixes for tiled F16 accuracy ensure reliable model execution on supported hardware.
llama.cpp has released version b11680, focusing heavily on Hexagon NPU optimization and broader hardware support. Key changes include a rework of tensor repacking for tiled and flat quantized weights, vectorization of raw k-quant ukernels, and fixes for accuracy regressions in tiled F16 matrix multiplication. The release also adds official builds for CUDA 13 (versions 12.8 and 13.4) and ROCm 10.0, while maintaining support for Vulkan, OpenVINO, and SYCL across Linux, Windows, macOS, and Android platforms.
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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 b11044 updates Hexagon and CUDA builds
llama.cpp Releases · September 19, 2026 · Same story
- 2llama.cpp b11054 adds Hexagon TOP_K support
llama.cpp Releases · September 20, 2026 · Same story
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Open the full story →llama.cpp b11680 Hexagon NPU and CUDA 13 support
3 developments
- Oct 16 · llama.cpp Releases
llama.cpp b11680 Hexagon NPU and CUDA 13 support (This article) - Oct 20 · llama.cpp Releases
llama.cpp updates Hexagon support for Snapdragon v85 - Oct 24 · llama.cpp Releases
llama.cpp b11849 adds Hexagon SGN op
More from llama.cpp Releases
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llama.cpp b12022 adds ROCm 10 and Snapdragon support
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.
llama.cpp Releases
Coding Toolscoding
llama.cpp b12023 fixes shared prefix bug
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.
llama.cpp Releases
Coding Toolscoding
llama.cpp b12024 adds Snapdragon and ROCm 10
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.
llama.cpp Releases
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