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Running Tasks on GPU

You can run any code on a cloud GPU by passing a gpu argument in your function decorator. 
from beam import endpoint


@endpoint(gpu="H100")
def handler():
    # Prints the available GPU drivers
    import subprocess
    print(subprocess.check_output(["nvidia-smi"], shell=True))

    return {"gpu":"true"}

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Available GPUs

Currently available GPU options are:
  • A10G (24Gi)
  • RTX4090 (24Gi)
  • H100 (80Gi)

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Check GPU Availability

Run beam machine list to check whether a machine is available. 
$ beam machine list

  GPU Type   Available
 ──────────────────────
  A10G          ✅
  RTX4090       ✅

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Prioritizing GPU Types

You can split traffic across multiple GPUs by passing a list to the gpu parameter. The list is ordered by priority. You can choose which GPUs to prioritize by specifying them at the front of the list. 
gpu=["T4", "A10G", "H100"]
In this example, the T4 is prioritized over the A10G, followed by the H100.

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Using Multiple GPUs

You can run workloads across multiple GPUs by using the gpu_count parameter.
This feature is available by request only. Please send us a message in Slack, and we’ll enable it on your account.
from beam import endpoint


@endpoint(gpu="A10G", gpu_count=2)
def handler():
    return {"hello": "world"}

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GPU Regions

Beam runs on servers distributed around the world, with primary locations in the United States, Europe, and Asia. If you would like your workloads to run in a specific region of the globe, please reach out.