> ## 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.

# DigitalOcean

> On-demand H100 GPU Droplets in fixed 1x and 8x shapes, with per-second billing and about one minute provisioning.

DigitalOcean GPU Droplets are on-demand single- and 8-GPU VMs with per-second billing and fast (about one minute) provisioning.

## GPU types

| GPU type    | VRAM  | Architecture  | GPUs available |
| ----------- | ----- | ------------- | -------------- |
| `H100-80GB` | 80 GB | NVIDIA Hopper | 1 or 8         |

DigitalOcean sells fixed 1x and 8x droplet shapes only — a `gpu_count` of 2 or 4 is rejected at submit.

## Submitting a job

```python theme={null}
job = client.training_jobs.create(
    base_model="Qwen/Qwen2.5-0.5B-Instruct",
    dataset_id=dataset.id,
    method="sft_text",
    output_name="qwen-do-sft",
    gpu_type="H100-80GB",
    gpu_count=1,
    provider="digitalocean",
)
```

## Notes

<Note>
  DigitalOcean also lists AMD MI300X droplets, but they are not yet enabled on Veri — a `provider="digitalocean"` submit with `gpu_type="MI300X-192GB"` returns a 400 listing the offered shapes. For AMD MI300X training today, use `provider="hotaisle"`.
</Note>

<Warning>
  DigitalOcean GPU capacity fluctuates minute-to-minute. A submit that hits `CAPACITY_UNAVAILABLE` after retrying across regions is not billed — check `veri gpu list --provider digitalocean` and retry.
</Warning>

`veri gpu list` shows one `AVAILABLE` entry per region with current capacity, or a single `UNAVAILABLE` row when a shape has no capacity anywhere. This signal comes from DigitalOcean's size catalog, which can lag real launch acceptance in both directions.

## 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="SFT (text)" icon="book" href="/training/sft">
    Supervised finetuning — a good fit for fast single-GPU droplets.
  </Card>
</CardGroup>