Run serverless GPU inference on Modal with auto-scaling to zero for an LLM
domain: modal.com/docs · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Install Modal: pip install modal and authenticate with modal setup
Define an App and an image with required dependencies: app = modal.App(); image = modal.Image.debian_slim().pip_install('vllm')
Decorate a class or function with @app.function(gpu='A100', image=image) to request a specific GPU — use 'H100:4' for 4x H100s
Load the model in a @modal.enter() method on a class-based deployment so weights are loaded once per container, not per request
Deploy with modal deploy your_file.py for persistent endpoints or modal run for one-off executions
Modal bills per millisecond of actual execution with no idle charges — containers scale to zero between requests automatically
Known gotchas
Cold starts from zero can take tens of seconds for large models — use GPU Memory Snapshotting (available since late 2025) to cache model state and reduce cold start time
The gpu parameter accepts type strings like 'A100', 'A100-80GB', 'H100', or count+type like 'H100:8' — check Modal docs for current availability by region
modal run executes once and exits; modal deploy creates a persistent webhook endpoint — use deploy for production inference APIs
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