
JetBrains has introduced Mellum2, a 12 billion parameter Mixture-of-Experts model optimized for low-latency text and code workloads. The model activates only 2.5 billion parameters per token, achieving over twice the inference speed of comparable models. Mellum2 is open-source under the Apache 2.0 license and is designed for high-throughput tasks in software engineering, such as code generation and summarization. This release highlights a trend towards specialized models that improve the efficiency and control of AI systems without replacing existing components.
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.