The b9947 release of llama.cpp has been announced, focusing on expanding platform support rather than introducing new features. Key updates include the addition of ROCm 7.2 support for Ubuntu x64, enhancing options for AMD GPU users. The release also maintains compatibility across various systems, including Windows with CUDA 13 and Ubuntu with Vulkan. While KleidiAI support for Apple Silicon is disabled, the update reinforces llama.cpp's position as a flexible tool for AI inference across diverse environments.
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llama.cpp Releases · June 26, 2026 · Same story
llama.cpp Releases · June 26, 2026 · Same story
This release quietly fixes a critical accuracy gap for ModernBERT encoders by implementing exact GELU activation, ensuring semantic embeddings match the original PyTorch models rather than approximations. It also brings native support for CUDA 13.4 across Linux and Windows, closing the driver compatibility lag that has plagued NVIDIA users on newer hardware stacks. While KleidiAI builds are temporarily disabled on Apple Silicon, the broader expansion to ROCm 10.0 and Snapdragon NPU keeps llama.cpp as the most versatile local inference runtime available today.
This release targets a specific but painful stability issue for Android users running llama.cpp on Qualcomm Adreno A6X GPUs. The kernel compiler was crashing due to argument limits in the iot device backend, effectively breaking local inference on those chips. By skipping the problematic kernel and adding explicit detection for the Adreno 623, the team restores functionality where it previously failed hard. It’s a narrow fix, but essential for anyone trying to run models on mid-range Android hardware without hitting compiler errors.
This release quietly sharpens llama.cpp’s performance on NVIDIA GPUs by fusing state snapshot copies into the recurrent cache during SSM scans. It also removes redundant CUDA copies in specific non-speculative decoding scenarios, shaving off latency where it counts. On the AMD side, ROCm 10.0 support arrives alongside stable builds for CUDA 12.8 and 13.4, keeping the library competitive across hardware vendors. KleidiAI on Apple Silicon is temporarily disabled, a minor setback for Mac users until that integration is stabilized. The net result is faster inference for SSM-based models without changing the user experience.
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