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Baseten trains models on managed GPUs and deploys the resulting checkpoints to production inference on the same platform. Train directly with Loops, a Python SDK for LoRA fine-tuning and RL on a curated set of base models, or bring your own container and training code with Training Jobs.

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Choose your path

  • Fine-tuning or running RL on a supported base model: Loops provisions a dedicated trainer and paired sampler, and each training step is an API call from a Python loop you write. You bring the data; Baseten runs the infrastructure. Start with the Loops overview.
  • Already training elsewhere: Your Axolotl config, TRL script, or custom loop runs unchanged in a container. Baseten provisions the GPUs, syncs checkpoints as your job saves them, and deploys any checkpoint as a production endpoint. That’s Training Jobs.
If you match both or neither, the paths differ in who drives. With Training Jobs, you hand Baseten a program: a batch job that runs to completion on hardware you declare. With Loops, your program calls Baseten: each training step is an API call to a live trainer, and a paired sampler serves the latest weights throughout. Loops covers LoRA fine-tuning and RL on a curated model list and is in early access; request access for your workspace. Training Jobs runs any training code and is available to every workspace today.

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Next steps

Loops quickstart

Train a step, sample from the tuned weights, and deploy a checkpoint.

Jobs quickstart

Run your first training job and deploy the result.

ML Cookbook

Production-ready examples for frameworks and models.