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llama.cpp b11194 adds ROCm 10 and Snapdragon support
llama.cpp Releaseshigh confidence
Why it matters
- →ROCm 10 support removes barriers for AMD GPU users wanting to run local LLMs efficiently.
- →Snapdragon Linux binary enables on-device inference for ARM-based mobile and edge devices.
- →CUDA 13.4 updates ensure compatibility with the latest NVIDIA driver stacks.
llama.cpp has released version b11194, expanding hardware support for local large language model inference. The update adds ROCm 10.0 binaries for Ubuntu and Windows x64, enabling direct compatibility with AMD Radeon GPUs. It also introduces a new Linux arm64 binary optimized for Snapdragon platforms, leveraging CPU, Adreno GPU, and Hexagon NPU acceleration. Additionally, the release includes updated CUDA 13.4 libraries for both Linux and Windows, alongside OpenVINO and SYCL builds. This update broadens the range of consumer hardware capable of running local AI models without proprietary drivers.
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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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