llama.cpp b10433 syncs ggml, expands hardware support across platforms
The popular local LLM runtime just dropped b10433 with a fresh ggml sync and new build targets.
llama.cpp, the wildly popular C/C++ implementation for running large language models locally, has shipped version b10433. Released August 14 by the ggml-org maintainers, this build's core change is a synchronization with the underlying ggml tensor library, which powers the optimized matrix operations and memory management that make local inference practical. With over 124,000 GitHub stars and 21,700 forks, llama.cpp remains the go-to tool for developers and hobbyists who want to run models like Llama 3, Mistral, and Phi on consumer hardware without cloud dependencies.
The release brings a comprehensive matrix of prebuilt binaries across major platforms. Mac users get Apple Silicon builds including an optimized variant with KleidiAI enabled, while Intel macOS builds are marked disabled. Linux users receive CPU, Vulkan, OpenVINO, and SYCL variants, with some ROCm builds disabled. Windows covers CPU, CUDA 12 and 13, Vulkan, OpenVINO, SYCL, and a preview ARM64 CUDA build. Mobile and embedded support includes Android arm64 CPU and iOS XCFramework, plus UI asset updates. For professionals running on-prem or edge LLM inference, b10433 ensures compatibility with the latest ggml optimizations across NVIDIA, AMD, and Adreno GPUs, making local AI deployment smoother across diverse infrastructure.
- b10433 is a ggml sync release, updating the core tensor library used for local LLM inference
- New binaries target Windows CUDA 13, Apple Silicon KleidiAI, and Android arm64
- Some builds (Intel macOS, ROCm 7.14) are marked disabled in this release
- llama.cpp has 124k GitHub stars, reflecting its dominance in local LLM runtime
Why It Matters
Keeps local LLM runtime current with ggml, enabling efficient on-device inference across CPUs and GPUs.