---
title: "GPU Function Execution"
tag: "Early access"
sidebarTitle: "GPU Functions"
description: "How a GPU Function's worker gets scheduled onto a GPU-capable node, and what happens when none is free."
icon: microchip
keywords: ["gpu", "gpu class", "scheduling", "function", "udf", "kubernetes", "node selector"]
availability:
  embedded: unavailable
  enterprise: available
---

<Badge color="purple">Early access</Badge>

[Registering a GPU Function](/curation/create-function#gpu) sets `gpu=True`
and an optional `gpu_class`; this page covers what happens after that, when
a job actually needs a worker. An operator configures one or more named
**GPU classes** for the deployment -- each one a Kubernetes node selector,
toleration set, and resource name (such as `nvidia.com/gpu`) pointing at a
specific pool of GPU nodes, plus a hardware description (GPU model, GPU
memory, how many GPUs one worker may request). A Function with no
`gpu_class` uses the deployment's `default` class.

## Placement {#placement}

When a job needs to run a GPU Function, the agent resolves its `gpu_class`
(or `default`) to that class's node selector, tolerations, and resource
name, and requests a worker pod shaped accordingly -- this is a separate
placement path from the ordinary CPU worker ladder used by non-GPU
Functions and other background jobs (see
the [Job Framework](/basics/jobs) for that general job
machinery). The resulting pod can only schedule onto a node matching the
class's selector and tolerations, so a class is really naming a pool of
nodes, not just a resource count.

## Waiting for capacity {#capacity}

A GPU class describes the deployment's hardware, not live availability --
listing classes never tells you whether a GPU is free right now. If no
node in the class has room, the worker pod stays pending rather than
failing the job outright or falling back to a CPU worker: the job reports
that it is waiting on that GPU class's capacity, and starts running as soon
as a matching node has room. A job stuck waiting indefinitely usually means
the class's node pool needs more capacity, not that anything is
misconfigured.

{/*
Draft for the list_gpu_classes release. Keep this section unpublished
until that API ships -- v0.41.0 (the latest release) does not have it,
and there is no SQL equivalent; discovery is a REST call
(`GET /v1/gpu/classes`), not a SQL statement.

## Inspecting GPU classes {#inspecting}

<CodeGroup>
```python Python icon="python"
for gpu in db.list_gpu_classes():
    print(gpu.name, gpu.gpu_model, gpu.gpu_memory_bytes, gpu.max_gpu_count_per_worker)
```
</CodeGroup>

Returns the operator-configured classes: name, resource name, GPU model
and memory, and the largest per-worker GPU count the class permits.
Listing requires the System Usage privilege.
*/}

## What to read next {#next}

<CardGroup cols={2}>
  <Card title="Create a Function" icon="code" href="/curation/create-function#gpu">
    Register a GPU Function and pin it to a specific class.
  </Card>
  <Card title="Job Framework" icon="list-check" href="/basics/jobs">
    The job types a deployment runs on its own schedule.
  </Card>
  <Card title="GPU Refresh Execution" icon="microchip" href="/enterprise/gpu-refresh-execution">
    The same placement path, for a computed column or MV refresh instead of a direct call.
  </Card>
</CardGroup>