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

# Overview

> GPU instances are full Linux containers with dedicated GPUs and SSH access.

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GPU Instances are full Linux containers with dedicated GPU hardware, root access, and a public IP address. They are ideal for training models, running experiments, fine-tuning, and any workload that needs direct access to GPU compute.

## What You Get

Every GPU instance includes:

* **Root access** -- Full `sudo` privileges in an isolated Linux container.
* **Dedicated GPUs** -- One or more GPUs exclusively allocated to your instance.
* **Configurable specs** -- Choose your CPU cores, memory, and disk size.
* **Public IP address** -- Direct SSH access and the ability to expose services.
* **Pre-installed tools** -- CUDA drivers, Python, pip, and common ML libraries come ready to use.

## Available GPUs

| GPU      | VRAM  | Best For                                    | Starting At |
| -------- | ----- | ------------------------------------------- | ----------- |
| RTX 4090 | 24 GB | Inference, fine-tuning, small training runs | \~\$0.35/hr |
| L40S     | 48 GB | Large model inference, medium training      | \~\$0.80/hr |
| A100     | 40 GB | Training, fine-tuning, research             | \~\$1.20/hr |
| A100     | 80 GB | Large-scale training, multi-GPU workloads   | \~\$1.60/hr |
| H100     | 80 GB | Maximum performance training and inference  | \~\$2.50/hr |

<Note>
  Prices shown are approximate starting rates and may vary based on availability and configuration. Check the [Deploy page](https://runcrate.ai/dashboard/instances/deploy) for current pricing.
</Note>

## Instance Lifecycle

Every instance moves through these states:

```
configured → creating → deploying → deployed → stopping → stopped → terminated
```

| Status         | Description                                            |
| -------------- | ------------------------------------------------------ |
| **Configured** | Instance configuration is saved but not yet submitted. |
| **Creating**   | Resources are being allocated.                         |
| **Deploying**  | The container is starting up and installing software.  |
| **Deployed**   | The instance is running and accessible via SSH.        |
| **Stopping**   | The instance is shutting down.                         |
| **Stopped**    | The instance is paused. Resources are still reserved.  |
| **Terminated** | The instance is permanently destroyed.                 |

## Billing

Instances are billed **hourly** based on the GPU type and configuration you select.

* Billing **starts** when the instance reaches the `deployed` state.
* Billing **stops** when the instance is `terminated`.
* Charges are deducted from your credit balance automatically.

<Warning>
  **Terminating an instance permanently deletes all local disk data.** There is no way to recover files after termination. Use [Storage Volumes](/storage/overview) or transfer your files before terminating.
</Warning>

## Next Steps

<CardGroup cols={2}>
  <Card title="Deploy an Instance" icon="rocket" href="/instances/deploying">
    Step-by-step deployment guide.
  </Card>

  <Card title="Connect via SSH" icon="terminal" href="/instances/connecting">
    SSH, VS Code, and port forwarding.
  </Card>
</CardGroup>