Together Post-Training: frontier-grade model shaping, the way that fits you
Post-train open LLMs like Qwen, GLM and Kimi into custom models you own, with SFT or RL. You write the training loop. Together runs the hard infrastructure.
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Together AI
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Meta Muse Spark Open is live on Together AI
Meta Muse Spark Open, Meta's open-weight model for agentic coding, is live on Together AI with a 1M-token context window on serverless and dedicated infrastructure.
Expanding our enterprise inference capacity with IBM Cloud and NVIDIA
Enterprises can now run open models at production scale on a dedicated B300 inference cluster, built by Together AI, IBM Cloud, and NVIDIA
Together Link: open models in the harness you already use. Start with one command today.
Together Link brings frontier open models like GLM 5.3 and Kimi K3 into the coding agent your team already uses, cutting model spend by over 50%.
How to train your own Jev for $17
We just launched our own Jev-like classifier, together/Tev1-4B-experimental, on top of Qwen3.5 4B on Together’s serverless platform. In this blog post we’ll show you how to fine-tune your own version!
Canary rollouts: upgrade models in production without downtime
A hard model swap exposes every user at once, and rolling back means cold-starting the old deployment under pressure. Here's how staged traffic ramps, metric gates, and automatic rollback work on dedicated inference.
How a global fintech scaled coding agent traffic with Dedicated Model Inference
Inside a global bank's shift to self-serve dedicated inference: how Together's DMI gave engineering teams direct control over scaling, models, and testing.
Migrating from closed to open source models, Together
Moving from closed to open source models can take weeks, not years. A five-stage playbook: discover, evaluate, adapt, decide, and production.
Together AI expands fine-tuning service with more models, live metrics, and finer controls
Together Fine-Tuning adds the latest open-weight models, live experiment tracking, Expert LoRA, early stopping, tokenized dataset previews, pre-flight validation, and lower training prices on selected models.
Introducing preemptible compute: the same compute, half the price
Together GPU Clusters now supports preemptible compute: the same GPU capacity at a flat 50% of the on-demand rate, with a five-minute drain window.
To Infinity and Beyond: ThunderKittens Now on NVIDIA Vera Rubin NVL72!
We ported ThunderKittens to NVIDIA's Vera Rubin NVL72 and rebuilt our NVFP4 GEMM around the new hardware, taking it from 42% of roofline to over 22 PFLOPS — competitive with cuBLAS and CuTe DSL. Here is what changed in the ISA and how we used it.
The Open Source AI Stack
A deep dive into the open model AI stack — model, inference, gateways and routers, harness, and tools — and how keeping each layer independent lets you swap in a new open model in minutes instead of rebuilding your workflow.
We ran 900 DeepSWE rollouts on GLM-5.3 and GLM-5.3 Flash. Flash gives up 5.6 points of pass@1 at …
We ran 900 DeepSWE rollouts on GLM-5.3 and GLM-5.3 Flash. Flash gives up 5.6 points of pass@1 at 17x lower cost, and only 2.6 points at pass@4.