Deploy any Hugging Face model as an API

Go from a Hugging Face model ID to a live, autoscaling endpoint with one Python file. Beam builds the container, manages the GPUs, and caches your weights between requests.

deply
$ beam deploy app.py:predict
=> Building image
=> Using cached image
=> Syncing files
=> Deployed
=> https://hf-inference-abc123.app.beam.cloud

# first cold start — on_start pulls weights from the Hub
Downloading Qwen/Qwen2.5-1.5B-Instruct → volume ./weights

# every boot after — cache hit, skip the download
Loaded weights from ./weights in 1.2s

Why Beam

The shortest path from Hub to production

No Dockerfiles, no serving framework to stand up. Declare your dependencies in Python and Beam handles the rest.

One file, no Dockerfiles
Declare Python packages and base images inline. Beam builds and caches the container for you.

Load once, serve many
on_start runs when the container boots, so models load once and every request hits warm weights.

Weights cached on volumes
Snapshot a model into a persistent volume once and skip the Hub download on every boot. Storage is included, free.

Gated & private models
Store your Hugging Face token with Beam's secrets manager and pull gated repos like Llama securely.

Live-reload development
beam serve runs your endpoint in the cloud while you edit locally — the same environment you'll ship to production.

Autoscaling built in
Endpoints scale with request volume and back to zero when idle — no capacity planning required.

How it works

Ship a model in three steps

01

Pick a model

Reference any Hub model ID from transformers, diffusers, or sentence-transformers — or bring your own weights.

02

Wrap it in an endpoint

Add the @endpoint decorator with your GPU and image. Load the model in on_start so it stays warm.

03

Serve, then deploy

Iterate with beam serve's live reload, then beam deploy gives you a production HTTPS endpoint.

terminal
# Develop against the cloud with live reload
$ beam serve app.py:predict
=> Watching for changes...

# Deploy to production
$ beam deploy app.py:predict
=> Deployed 🎉
=> https://hf-inference-abc123.app.beam.cloud

$ curl -X POST https://hf-inference-abc123.app.beam.cloud \
    -H 'Authorization: Bearer YOUR_TOKEN' \
    -d '{"prompt": "Explain serverless GPUs"}'

FAQ

Which libraries are supported?

Anything pip-installable: transformers, diffusers, sentence-transformers, ultralytics, or your own packages. You define the image in Python and Beam builds it.

Can I deploy gated or private models?

Yes. Save your Hugging Face token with beam secret create HF_TOKEN and the container authenticates to the Hub securely.

Do weights re-download on every cold start?

Not if you cache them. Download weights to a persistent volume once, and every container loads from disk instead of the Hub.

How do I pick the right GPU?

Start small — a T4 handles many models under a few billion parameters — and watch real-time GPU utilization in the Beam dashboard to right-size from there.

What about chat models with an OpenAI-style API?

Use Beam's vLLM integration: it serves any compatible Hub model behind an OpenAI-compatible API, so existing clients work unchanged.