llama.cpp has released version b11884, expanding hardware support across Linux, Windows, and openEuler platforms. Key additions include ROCm 10.0 for AMD GPUs on both Linux and Windows, as well as CUDA 13.4 builds for NVIDIA users. The release also introduces native support for Linux arm64 Snapdragon devices, enabling inference on mobile architectures with CPU, Adreno GPU, and Hexagon NPU acceleration. Conversely, macOS Apple Silicon KleidiAI builds are disabled in this version. openEuler support remains limited to specific Huawei Ascend configurations (310p and 910b).
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llama.cpp Releases · October 22, 2026 · Same story
llama.cpp Releases · October 26, 2026 · Same story
This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.
This release quietly expands llama.cpp's hardware support to include Qualcomm Snapdragon devices with CPU, Adreno GPU, and Hexagon NPU acceleration. It also updates ROCm to version 10.0 for both Linux and Windows, keeping AMD users in step with the latest driver stack. CUDA builds now default to 13.4 libraries alongside 12.x options, reflecting the industry's gradual shift toward newer NVIDIA toolkits. While no new model architectures are introduced, the binary matrix grows significantly, ensuring local inference remains viable on a wider array of edge and server hardware.
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