The b10173 release of llama.cpp has been announced, featuring expanded support for various platforms. This update includes Vulkan support for both Ubuntu and Windows, as well as ROCm 7.2 for Ubuntu, enhancing GPU compatibility. While no new model architectures are introduced, the release focuses on broadening hardware support, making llama.cpp a more versatile tool for developers. This expansion allows for more efficient use of llama.cpp across different systems, reinforcing its utility as a flexible inference runtime.
Read originalThe latest b10156 release of llama.cpp continues its trend of broadening platform compatibility, notably adding support for ROCm 7.2 on Ubuntu x64. This update ensures that AMD GPU users can leverage llama.cpp more effectively, narrowing the gap with NVIDIA's CUDA. The release also includes Vulkan support for both Ubuntu and Windows, enhancing the versatility of the software for developers. While no new models or quantization methods are introduced, this update solidifies llama.cpp's position as a versatile inference runtime across diverse hardware configurations.
The latest b10158 release of llama.cpp continues its trend of broadening platform compatibility, though without major new features. Notably, the release includes support for ROCm 7.2 on Ubuntu x64, which is significant for AMD GPU users seeking alternatives to NVIDIA's CUDA. While KleidiAI support for Apple Silicon remains disabled, the release still covers a wide array of platforms, including Windows and openEuler. This update demonstrates llama.cpp's commitment to being a versatile inference runtime across diverse hardware configurations.
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.