
A study by ReasonIF reveals that frontier large reasoning models (LRMs) fail to follow reasoning instructions over 75% of the time, introducing a new benchmark across various parameters.
Read original
© Together AI BlogThunderAgent 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 BlogTogether AI's partnership with Moonshot AI marks a significant step in making cutting-edge AI models more accessible to developers. By hosting Moonshot's Kimi models, including the 2.8 trillion parameter Kimi K3, Together AI offers developers immediate access to powerful open-weight models. This collaboration allows for seamless integration and post-training capabilities, enabling developers to fine-tune models for specific applications. The partnership promises to deliver high-performance AI solutions with the flexibility and scalability that open models provide, challenging proprietary systems in the market.