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llama.cpp b11185 adds ROCm 10 and CUDA 13
llama.cpp Releaseshigh confidence
Why it matters
- →ROCm 10.0 support removes friction for AMD GPU users running local LLMs.
- →CUDA 13 compatibility prepares the ecosystem for upcoming NVIDIA hardware generations.
- →Snapdragon Linux support extends viable inference options to ARM-based edge devices.
llama.cpp has released build b11185, focusing on expanding hardware compatibility rather than introducing new model formats or quantization methods. Key additions include ROCm 10.0 support for AMD GPUs on Linux and Windows, as well as CUDA 13.4 libraries for NVIDIA users on Ubuntu and Windows. The release also adds Snapdragon CPU/GPU/NPU support for Linux arm64 systems. These changes aim to standardize inference across diverse hardware ecosystems without requiring manual configuration flags.
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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 fixes LFM2 audio transcription accuracy
A subtle but critical fix in llama.cpp’s mtmd module corrects the mel preprocessor for the LFM2 audio model, eliminating transcription errors that affected over half of tested Japanese utterances. By switching to a symmetric Hann window and adjusting normalization epsilon, the build now matches reference implementations with near-zero error rates instead of changing entire words. This resolves a significant reliability gap for local multilingual speech-to-text, ensuring that open-source inference produces results comparable to proprietary baselines without manual intervention.
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