Models & Labs
llama.cpp b11221 release with ROCm 10 and CUDA 13
llama.cpp Releaseshigh confidence
Why it matters
- →ROCm 10.0 support allows AMD GPU users to run the latest inference workloads without manual compilation.
- →CUDA 13.4 builds ensure compatibility with NVIDIA's newest driver releases for both Linux and Windows.
- →Snapdragon arm64 support opens local inference possibilities for ARM-based mobile and edge hardware.
llama.cpp has released version b11221, updating its binary distribution to support ROCm 10.0 and CUDA 13.4 across Linux and Windows platforms. The release also adds initial support for Linux arm64 Snapdragon devices, expanding hardware compatibility beyond x86 and Apple Silicon. Notably, KleidiAI builds for macOS Apple Silicon and openEuler configurations have been disabled in this iteration. This update ensures compatibility with the latest GPU driver ecosystems while maintaining broad cross-platform availability.
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Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
- 1llama.cpp b11105 adds ROCm 10 and Snapdragon support
llama.cpp Releases · September 22, 2026 · Same story
- 2llama.cpp b11222 release with ROCm 10 and CUDA 13
llama.cpp Releases · September 28, 2026 · Same story
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llama.cpp b11213 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 for modern hardware, 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 inference on high-performance mobile SoCs via CPU, Adreno GPU, and Hexagon NPU acceleration. While KleidiAI on Apple Silicon has been disabled in this specific binary set, the broader platform coverage signals a strategic shift toward hardware agnosticism that benefits anyone running models outside of standard NVIDIA setups.
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This release quietly sharpens llama.cpp’s edge on AMD hardware by enabling the fattn-mma kernel for large query dimensions on CDNA architectures. It specifically targets high-throughput scenarios where batch sizes push dkq beyond 256, a common bottleneck in serving workloads. By optimizing these specific matrix multiplication paths, the update reduces latency and improves throughput for enterprise-style inference without requiring code changes. This is another step in making AMD GPUs competitive with NVIDIA for heavy lifting.
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llama.cpp b11215 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 Snapdragon binary for Linux, which unlocks local AI on ARM-based laptops using Adreno GPUs and Hexagon NPUs. While KleidiAI on macOS has been disabled in this build, the expansion to AMD and Qualcomm hardware makes this one of the most significant platform broadening efforts yet.
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