Fast and cheap inference post-trained on your workload.
We turn your traces into an RL environment, build the evals, and post-train an open model automatically. You just change your base URL.
We rebuild your production traces as an RL environment, derive evals from what actually succeeded in production, and post-train an open model against them.
Post-training a model on your own workload is normally a staffing problem. You need people who do RL for a living, and a cluster you pay for whether or not it's busy. Gradiated completely removes the need for that part. It's just an API endpoint, and your production traffic.
Trained on your production traffic
https://api.openai.com/v1
Production traces
RL environment
Evals
Post-training
Available on your favourite models
All the way down
to the silicon
Waste in inference isn't in one place. It comes from model architectures that thrash memory, kernels that leave chips half-idle, and schedulers that keep expensive capacity waiting. Where possible, the workload should shape the silicon.
We design chips for specific model families and provide the weights we generate. Through hardware optimization, our goal is to be the most efficient, configurable, and reliable place to run inference for those base model families.
Build with us