Models & Labs
llama.cpp b9276 release enhances prompt monitoring
llama.cpp Releaseshigh confidence
Why it matters
- →Exposing prompt token counts improves transparency for developers.
- →It allows for better monitoring and optimization of prompt processing.
- →Enhances usability by providing insights into internal processing stages.
The b9276 release of llama.cpp now exposes prompt token counts in the /slots endpoint, allowing developers to monitor prompt evaluation progress. This update includes new fields such as n_prompt_tokens, n_prompt_tokens_processed, and n_prompt_tokens_cache, which were previously tracked internally but not accessible. This enhancement provides developers with better insights into the processing stages, improving the usability and transparency of the llama.cpp framework.
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Llama.cpp adds GLM-5.2 speculative decoding support
Llama.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.
llama.cpp Releases
Open Sourcemodels
llama.cpp b10175 Release Expands Platform Support
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.
