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llama.cpp b11140 optimizes sparse attention on CUDA
llama.cpp Releaseshigh confidence
Why it matters
- →Sparse attention optimization significantly reduces latency for long-context inference on NVIDIA GPUs.
- →Kernel unrolling at compile time eliminates runtime overhead in the query loop.
- →Improvements are included in standard CUDA builds, requiring no user configuration changes.
llama.cpp b11140 delivers targeted performance improvements for NVIDIA GPU inference, specifically optimizing the sparse flash attention prefill step. By templating kernels to unroll loops at compile time, batched sparse operations on 49k context windows drop from 586 microseconds to 244 microseconds. The release also includes standard platform binaries for CUDA 12/13, ROCm 10.0, and various CPU architectures, with KleidiAI builds disabled on macOS as per previous stability decisions.
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llama.cpp b11146 adds CUDA 13 and Snapdragon support
The latest llama.cpp build brings immediate relevance to users on bleeding-edge NVIDIA hardware with native CUDA 13.4 support across Linux and Windows, closing the gap for those testing next-gen GPU architectures. More notably, it finally addresses the mobile inference landscape by including a dedicated build for Linux arm64 Snapdragon devices, covering CPU, Adreno GPU, and Hexagon NPU paths. This moves local AI beyond just desktop GPUs into the realm of high-performance edge computing on Qualcomm silicon. While Apple Silicon builds have KleidiAI disabled in this specific release, the expansion to ARM-based mobile NPUs marks a significant shift in where llama.cpp can run efficiently.
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llama.cpp b11147 adds ROCm 10 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally bringing first-class ROCm 10.0 support to both Linux and Windows. AMD GPU users no longer need workarounds, effectively closing a long-standing parity gap with NVIDIA's CUDA ecosystem. Equally notable is the new Linux arm64 build targeting Snapdragon chips, which unlocks local LLM execution on high-performance mobile hardware via CPU, Adreno GPU, and Hexagon NPU acceleration. While KleidiAI on Apple Silicon has been disabled in this specific binary set, the broader platform expansion signals a shift toward heterogeneous computing that extends well beyond traditional desktop GPUs.
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llama.cpp b11149 adds backend testing and ROCm 10 support
This release quietly solidifies llama.cpp’s position as the universal inference runtime by adding explicit ROCm 10.0 builds for both Linux and Windows. The inclusion of CUDA 13.4 alongside the existing 12.x variants ensures compatibility with the latest NVIDIA driver stacks without forcing users to stick to older libraries. More importantly, the new backend testing infrastructure means these diverse hardware configurations are now validated systematically rather than left to chance. This reduces fragmentation for developers running on AMD or newer NVIDIA cards who previously had to troubleshoot build issues manually.
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