Together Fine-Tuning Platform Enhancements Announced

ThunderAgent Boosts Agentic Inference Efficiency

ThunderAgent introduces a novel approach to agentic inference, significantly improving throughput and reducing latency in synthetic data generation. By treating each agent workflow as a program rather than isolated requests, it mitigates KV cache thrashing and balances load across nodes. This results in up to 2.5× higher throughput on single nodes and near-linear scaling on multi-node clusters. ThunderAgent's compatibility with existing inference optimizations makes it a practical choice for enhancing large-scale agentic workloads.

Together AI partners with Moonshot AI for Kimi models

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 b10178 Release Adds Trace Logging

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.