GPU types

import runpod
from runpod import GpuGroup, GpuType

app = runpod.App("gpu-demo")

@app.queue(gpu="4090")                          # Short name
def a(): ...

@app.queue(gpu=GpuGroup.AMPERE_80)              # Any GPU in a pool
def b(): ...

@app.queue(gpu=GpuType.NVIDIA_H100_80GB_HBM3)   # One exact device
def c(): ...

@app.queue(gpu=["H100", GpuGroup.AMPERE_80])    # Several options
def d(): ...

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How GPU strings are matched

1. "any", which allows every GPU.
2. A pool ID such as "ADA_24" (case-insensitive).
3. An exact device name such as "NVIDIA B200", or its enum name such as "NVIDIA_B200".
4. A fragment of a device name, such as "4090", "B200", or "h100". Every device whose name contains the fragment matches, so "H100" selects all three H100 variants.

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GPU pools

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GPU types

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Choose a GPU

• For development, use GpuGroup.ANY or a list of several pools so workers start quickly even when one GPU type is scarce.
• For production, pin a specific pool or device so performance and cost are predictable.
• For multiple GPUs per worker, combine gpu with gpu_count, for example gpu="H100", gpu_count=4.
• For a minimum driver version, set min_cuda_version, for example min_cuda_version="12.8".

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Next steps

• Run on CPUs when you don’t need a GPU.
• See every resource parameter.