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llama.cpp b11192 adds ROCm 10 and Snapdragon support
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Why it matters
- →ROCm 10.0 support allows AMD GPU users to access modern inference performance without manual compilation.
- →Snapdragon arm64 support enables local AI on high-efficiency ARM laptops with NPU acceleration.
- →CUDA 13 availability ensures compatibility with the latest NVIDIA driver stacks for both x64 and arm64.
llama.cpp released build b11192, expanding hardware support across multiple platforms. Key additions include ROCm 10.0 binaries for Linux and Windows, enabling newer AMD GPUs to run local models efficiently. The release also introduces native support for Linux arm64 Snapdragon devices, supporting CPU, Adreno GPU, and Hexagon NPU acceleration. CUDA 13.4 libraries are now available for both x64 and arm64 architectures on Linux and Windows. Conversely, KleidiAI support on macOS Apple Silicon has been disabled in this build.
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llama.cpp b11183 splits Metal FA kernels
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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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