Together Post-Training: frontier-grade model shaping, the way that fits you

Together has launched Together Post-Training, a service for customizing open-weight AI models through supervised fine-tuning (SFT) and reinforcement learning (RL), designed to let developers control training loops, data, environments, rewards, and evaluation steps while Together manages underlying infrastructure and optimizations. The platform provides dedicated GPU-based Model Resources, supports LoRA and selected full-weight training, allows multiple LoRA experiments to share capacity, and includes Trainer and Generator components for model updates and rollout generation, including asynchronous RL workflows and custom objectives. It launches with models including Muse Spark Open, GLM 5.3, Kimi K3, and Qwen 3.8-27B, and emphasizes numerical consistency between training and inference, rollout optimizations, caching, routing, sequence packing, and configurable GPU resources to improve quality, speed, and cost efficiency. The service also supports multi-LoRA adapter deployment through Dedicated Model Inference and aims to preserve checkpoint quality when models are converted for lower-precision production serving. Together offers SDK documentation, example cookbooks, migration support for Tinker API users, optional assistance from forward-deployed engineers, and an introductory price of $9 per B200 GPU-hour for the first eight weeks.

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