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llama.cpp adds OpenCL A8 Q5_K binary kernels
llama.cpp Releaseshigh confidence
Why it matters
- →Enables efficient Q5_K inference on Qualcomm Adreno GPUs via pre-compiled OpenCL kernels.
- →Fixes transpose handling bugs that could affect accuracy or performance in specific bin kernel scenarios.
- →Expands official binary support for ROCm 10.0 and CUDA 13 across Linux and Windows.
llama.cpp has released a new build featuring added OpenCL binary kernels for Qualcomm A8 architectures, specifically supporting Q5_K quantization formats. This update includes fixes for s-transpose handling in bin kernels and expands support for various platforms including Ubuntu, Windows, and Android with CUDA 13 and ROCm 10.0. The release also maintains compatibility with existing hardware like Apple Silicon via KleidiAI (though currently disabled in this specific binary set) and Intel CPUs. This update primarily benefits developers optimizing local inference on mobile and embedded ARM devices.
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llama.cpp b11183 splits Metal FA kernels
This release quietly refactors how llama.cpp handles Flash Attention on Apple Silicon by splitting kernels into per-dtype libraries. It’s a structural optimization that likely reduces memory overhead and improves compilation times for Metal users, though the immediate performance gains are subtle compared to algorithmic leaps. The build matrix remains massive, adding ROCm 10.0 and CUDA 13.4 support while disabling KleidiAI on Apple Silicon for now. This is infrastructure maintenance rather than a feature breakthrough, but it keeps the runtime robust across the expanding landscape of hardware backends.
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llama.cpp b11184 expands Metal support to wider blocks
This release tackles a specific performance bottleneck on Apple Silicon by extending Metal FWHT kernels to handle block widths up to 8192. Previously limited to 512, these wider operations now use threadgroup memory instead of registers, enabling faster inference for larger context windows or model architectures that rely on Hadamard transforms. The update also ships binaries for CUDA 13 and ROCm 10.0, keeping the toolkit aligned with the latest NVIDIA and AMD driver ecosystems. It’s a quiet but necessary optimization that improves throughput on M-series chips without changing the user experience.
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llama.cpp b11185 adds ROCm 10 and CUDA 13
One of llama.cpp's quietest but biggest releases. With ROCm 10.0 added as a default build, AMD GPU users stop being second-class citizens for local inference — the gap with CUDA narrows visibly. Apple Silicon Macs now compile in KleidiAI by default, meaning every M-series machine gets ARM-tuned GEMM kernels for free, no flag-flipping required. There's no new model and no new quantization here — just llama.cpp quietly becoming the inference runtime for everyone who isn't on NVIDIA. That's the headline.
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