Generative media on serverless GPUs
Run text-to-video models like Mochi behind an API without reserving a single GPU. Beam provisions H100s per job, saves the rendered video, and returns a hosted URL.
Run large models without managing your own infra
Ship an AI video API
Upload your weights
Download the model to a persistent volume once — every container mounts it instantly afterward.
Deploy the pipeline
Wrap the diffusers pipeline in an endpoint or task queue with an H100 attached.
Host as HTTPS endpoint
POST a prompt and get a hosted video URL back — synchronously for short clips, via webhook for long renders.
# 1. Stage the weights on a volume (once)
$ python upload.py # snapshot_download() into the volume
# 2. Deploy the generation API
$ beam deploy app.py:generate_video
=> Deployed 🎉
=> https://mochi-1-abc123.app.beam.cloud
# 3. Render over HTTP
$ curl -X POST https://mochi-1-abc123.app.beam.cloud \
-H 'Authorization: Bearer YOUR_TOKEN' \
-d '{"prompt": "a hot air balloon drifting over glaciers"}'
{"url": "https://app.beam.cloud/output/id/77c1..."}Frequently asked questions
Which video models can I run?
Beam has a step-by-step Mochi-1 guide, and any open video model that runs in PyTorch — via diffusers or custom code — deploys the same way.
How do you handle multi-minute render times?
Run the model behind a task queue: requests return a task ID instantly, timeouts are configurable (or disabled), and a callback URL notifies your app when the render lands.
How do I keep costs under control?
Billing is per-second and containers scale to zero between renders, so you pay for render time only — never for an idle H100.
Where do model weights live?
On a persistent volume. Upload them once with snapshot_download, and every container loads from disk instead of pulling tens of gigabytes per boot.
How are finished videos delivered?
Save them with Output and return hosted public URLs from your API, or push them to your own storage from inside the container.
Related use cases
Run Stable Diffusion and Flux as an API
Host SDXL, Flux, and custom checkpoints behind an autoscaling API.
InferenceTurn ComfyUI workflows into production APIs
Turn ComfyUI workflows into autoscaling API endpoints.
Task QueuesFan out batch jobs across thousands of containers
Fan out batch inference and ETL across thousands of containers.
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.