> ## Documentation Index
> Fetch the complete documentation index at: https://docs.veri.studio/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS

> The default training provider: NVIDIA L4, A10G, A100, and H100 on EC2 with per-second billing, on-demand or spot.

AWS is the default provider and the most reliable for NVIDIA training. Omit the `provider` field entirely to use it.

## GPU types

| GPU type    | VRAM  | Instance      | GPUs per node |
| ----------- | ----- | ------------- | ------------- |
| `L4-24GB`   | 24 GB | g6.xlarge     | 1             |
| `A10G-24GB` | 24 GB | g5.xlarge     | 1             |
| `A100-80GB` | 80 GB | p4de.24xlarge | 8             |
| `H100-80GB` | 80 GB | p5.48xlarge   | 8             |

L4 and A10G are single-GPU instances — you rent one card. A100 and H100 are whole 8-GPU nodes: you get (and pay for) all 8 cards regardless of `gpu_count`, which controls how many are used for training, not how many are rented.

## Submitting a job

```python theme={null}
job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    reward_function_id=reward_fn.id,
    gpu_type="L4-24GB",
    gpu_count=1,
    # provider defaults to "aws"
)
```

## Notes

* Billing is per-second, on-demand or spot.
* Regions: AWS is the only provider with manual region selection — pass `region` at submit, or omit it for the default. Unknown regions 400 with the offered list.
* Multi-node gangs (`num_nodes >= 2`, custom-script only) are AWS-only and require the 8-GPU A100/H100 shapes.

## Where to go next

<CardGroup cols={2}>
  <Card title="All providers" icon="server" href="/training/providers">
    Compare GPUs, billing, and status across providers.
  </Card>

  <Card title="Custom scripts (L3)" icon="code" href="/training/custom-script">
    Bring your own training loop — runs on AWS NVIDIA GPUs.
  </Card>
</CardGroup>