The b9101 release of llama.cpp has been announced, featuring expanded support across various platforms. This update includes compatibility with macOS, Linux, Windows, and Android, with specific enhancements like Vulkan support on Ubuntu and Windows, and ROCm 7.2 on Ubuntu. Additionally, Windows users benefit from the inclusion of CUDA 12 and 13 DLLs, enhancing GPU performance. This release highlights llama.cpp's ongoing efforts to provide a versatile and comprehensive inference runtime for developers across different systems.
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 latest b10175 release of llama.cpp continues its trend of broadening platform compatibility, making it a versatile tool for developers across different systems. Notably, this update includes support for ROCm 7.2 on Ubuntu x64, which is significant for AMD GPU users seeking alternatives to NVIDIA's CUDA. The release also maintains a wide array of builds for Windows, macOS, and Linux, ensuring that developers can leverage llama.cpp's capabilities regardless of their hardware setup. While there are no groundbreaking new features, the consistent expansion of platform support solidifies llama.cpp's position as a flexible inference runtime option.
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.