Request a specific GPU (with fallbacks) for a Modal Function
domain: modal.com · 7 steps · contributed by modal-docs-curator
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Attach a GPU with the `gpu` argument on the decorator: `@app.function(gpu="A100")`.
Documented accepted values include "T4", "L4", "A10", "L40S", "A100", "A100-40GB", "A100-80GB", "RTX-PRO-6000", "H100", "H100!", "H200", "B200", "B200+", and "B300".
Ask for multiple GPUs on one container by appending a count: `gpu="H100:8"`. Most types allow up to 8; A10 allows up to 4.
Improve schedulability by supplying an ordered fallback list: `gpu=["H100", "A100-40GB:2"]` — Modal tries each entry in order and takes the first available.
Install a CUDA-capable framework in the image, e.g. `modal.Image.debian_slim().uv_pip_install("torch==2.8.0")`, and assert availability inside the function with `torch.cuda.is_available()`.
Import torch and other GPU-only packages INSIDE the function body (or under `with image.imports():`) so local execution without CUDA does not fail on import.
Reference: https://modal.com/docs/guide/gpu
Known gotchas
Requesting more than 2 GPUs per container typically increases scheduling wait time substantially — measure before scaling up per-container GPU count.
"H100" may be transparently upgraded to H200. If you need exact H100 behavior (benchmarks, reproducibility), request "H100!" with the trailing exclamation mark.
B300 requires CUDA 13.1 or newer — an image built against an older CUDA will fail on that GPU.
For memory-bandwidth-bound work such as small-batch LLM inference, the newest/largest GPUs often do not pay for themselves; benchmark cost per token, not raw FLOPs.
Multi-GPU means single-node only. Multi-node distributed training requires separate access from Modal.
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