
NVIDIA has announced a collaboration with Ineffable Intelligence, an AI lab founded by AlphaGo architect David Silver, to develop infrastructure for large-scale reinforcement learning. The partnership focuses on creating systems that learn continuously from experience, a step beyond traditional AI models. Utilizing NVIDIA's Grace Blackwell and the upcoming Vera Rubin platform, the project aims to build a pipeline that supports the unique demands of reinforcement learning. This effort could enable AI systems to autonomously discover new knowledge, potentially leading to significant advancements in AI capabilities.
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 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.