Fine-tune models on GPUs
Fine-tune LLMs and diffusion models on serverless H100s without touching infrastructure. Decorate your training function, run the file, and Beam handles the GPUs, storage, and scaling.
Fine-tuning without wrangling GPUs
Beam runs the compute, just bring your code. LoRA, QLoRA, and more with zero infrastructure to manage.
Launch a training job
Upload weights and data to a volume
Create a volume and download your base model and dataset.
Run training with one decorator
Wrap your training loop in @function, declare the GPU inline, and run the file. Beam streams logs to your terminal.
Serve the result
Point an endpoint at the fine-tuned weights in the same volume and deploy it as an autoscaling API.
# 1. Create a volume and stage weights + data
$ beam volume create gemma-ft
$ python upload.py # snapshot_download() into the volume
# 2. Kick off training on an H100
$ python finetune.py
=> Running function: <finetune:fine_tune>
{'loss': 1.617, 'epoch': 0.01}
...
=> Function complete
# 3. Inspect the artifacts
$ beam ls gemma-ft/gemma-2b-finetuned
adapter_config.json
adapter_model.safetensorsFrequently asked questions
Which fine-tuning methods work?
Anything that runs in PyTorch: LoRA and QLoRA with PEFT, Unsloth, TRL, or your own training loop. Beam provides the managed GPU runtime — the framework is up to you.
Do I pay when I'm not training?
No. For serverless, billing is per-second and stops when the run ends. Storage for weights and checkpoints is included, free of charge.
Where do my checkpoints go?
To a persistent storage volume mounted into the container. Inspect files with beam ls, download them, or serve them directly from an inference endpoint.
Can I fine-tune gated models like Llama or Gemma?
Yes. Save your Hugging Face token with beam secret create HF_TOKEN, and the container can pull gated repos securely.
What GPUs can I train on?
T4s and A10Gs up through A100s and B300s. Pick the GPU in the decorator and change it any time with one line.
Related use cases
Run GPU training jobs on-demand
Start training runs on cloud GPUs from Python — pay only while they run.
InferenceDeploy open-source LLMs
Serve open-source LLMs behind an autoscaling, OpenAI-compatible API.
InferenceRun Stable Diffusion and Flux as an API
Host SDXL, Flux, and custom checkpoints behind an autoscaling API.
Start shipping on infra
you won’t outgrow.
Run sandboxes and GPU workloads on your cloud, and scale out to ours when you need to. No infra to manage.