The b10175 release of llama.cpp has been announced, featuring expanded support across various platforms. This update includes ROCm 7.2 support for Ubuntu x64, enhancing options for AMD GPU users. The release continues to offer a wide range of builds for Windows, macOS, and Linux, catering to diverse developer needs. While no new model architectures are introduced, the focus remains on broadening compatibility and usability across different hardware configurations.
Read originalLlama.cpp's latest update introduces speculative decoding support for GLM-5.2, enhancing its capabilities with NextN/MTP features. This addition allows for more efficient tensor loading and context management, particularly benefiting models using the GLM_DSA architecture. The update also includes options for exporting models with or without the MTP feature, providing flexibility for developers. This release marks a step forward in optimizing model performance and adaptability, especially for those leveraging the GLM-5.2 framework.
The b10176 release of llama.cpp enhances its platform reach, notably adding ROCm 7.2 support on Ubuntu x64, which is a significant boost for AMD GPU users. This update continues to cater to a wide array of systems, from macOS to Windows and Linux, ensuring developers can deploy llama.cpp across various hardware setups. While there are no groundbreaking new features, the release solidifies llama.cpp's role as a flexible tool for AI inference. By improving compatibility and functionality, this update makes llama.cpp more accessible and practical for developers working with different systems.
The b10178 release of llama.cpp enhances its server capabilities by adding trace logging for slot similarity checking, offering developers detailed insights into prompt cache slot selection processes. This update includes specifics on skip reasons and similarity calculations, which can aid in performance optimization. While no new model architectures are introduced, the release continues to support a wide array of platforms, such as macOS with KleidiAI, Ubuntu with ROCm 7.2, and Windows with CUDA 12 and 13. This makes llama.cpp a more versatile tool for developers working on different systems, reinforcing its position as a comprehensive inference runtime.
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Grabette is a new open-source system designed to simplify the collection of robot manipulation data. By using a handheld gripper equipped with cameras, it allows users to record tasks without needing a robot or lab setup. This democratizes data collection, enabling anyone to contribute to a large, collaborative dataset. The system is built on standard, easily accessible components, making it accessible for widespread use. This release aims to address the data bottleneck in robot learning by encouraging community participation in building diverse datasets.
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