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

# GPU Setup

> Connect GPU compute providers, configure pod defaults, set budget limits, and optimize for cost.

# GPU Setup

This guide walks you through connecting GPU compute to your lab. You need GPU access to run experiments that require heavy computation (MCMC chains, model training, large-scale data processing).

## Connect RunPod

<Steps>
  <Step title="Get your RunPod API key">
    1. Go to [runpod.io](https://runpod.io) and sign in
    2. Navigate to **Settings > API Keys**
    3. Create a new API key with full access
    4. Copy the key
  </Step>

  <Step title="Add the key to Hubify">
    <Tabs>
      <Tab title="Web UI">
        1. Go to **Lab Settings > Compute**
        2. Click **Connect RunPod**
        3. Paste your API key
        4. Click **Verify & Save**
      </Tab>

      <Tab title="CLI">
        ```bash theme={null}
        hubify pod config --provider runpod --api-key "your-runpod-api-key"
        ```
      </Tab>
    </Tabs>
  </Step>

  <Step title="Verify the connection">
    ```bash theme={null}
    hubify pod config --test
    ```

    ```
    RunPod connection: OK
    Available GPUs: H200, H100, A100, A40
    Account balance: $245.00
    ```
  </Step>
</Steps>

## Set Default GPU

Configure which GPU type is used when experiments do not specify one:

```bash theme={null}
# Set default GPU
hubify pod config --default-gpu h100

# Set default timeout
hubify pod config --default-timeout 4h

# View current config
hubify pod config --show
```

## Budget Controls

Set spending limits to avoid surprises:

```bash theme={null}
# Monthly budget cap (pods queue when reached)
hubify pod budget --monthly 500

# Per-experiment cap
hubify pod budget --per-experiment 50

# Alert threshold (notify at 80% of budget)
hubify pod budget --alert-threshold 0.8
```

When the monthly budget is reached:

* New experiments queue instead of launching
* You receive a notification
* The orchestrator suggests cost-saving alternatives
* Running experiments continue until completion

## Pod Templates

Create reusable pod configurations for common experiment types:

```bash theme={null}
# Create a template
hubify pod template create \
  --name "mcmc-standard" \
  --gpu h100 \
  --timeout 4h \
  --docker-image "hubify/cosmo:latest" \
  --env "OMP_NUM_THREADS=16" \
  --storage 50GB

# Use a template
hubify experiment run --name "my-chain" --pod-template mcmc-standard

# List templates
hubify pod template list
```

## GPU Selection Guide

| Experiment Type                   | Recommended GPU | Why                            |
| --------------------------------- | --------------- | ------------------------------ |
| MCMC chains (\< 100K samples)     | H100            | Good balance of cost and speed |
| MCMC chains (> 100K samples)      | H200            | Large memory prevents OOM      |
| Neural network training           | H100 or H200    | Depends on model size          |
| Anomaly detection (large catalog) | H200            | 141 GB VRAM for full dataset   |
| Data preprocessing                | CPU             | No GPU needed, save money      |
| Figure generation                 | CPU or A40      | Lightweight, save money        |

## Persistent Storage

Configure persistent storage for datasets and results:

```bash theme={null}
# View storage usage
hubify pod storage list

# Upload a dataset (available to all pods)
hubify pod storage upload ./planck_likelihood.tar.gz

# Set retention policy
hubify pod storage config --retain-days 90
```

Persistent storage survives pod teardowns. Pre-stage large datasets here so experiments start instantly instead of waiting for downloads.

## SSH Keys

Add SSH keys for direct pod access:

```bash theme={null}
# Add your SSH key
hubify pod ssh-key add --file ~/.ssh/id_ed25519.pub

# List configured keys
hubify pod ssh-key list
```

## Monitoring

Monitor active pods from Captain View or CLI:

```bash theme={null}
# Real-time pod status
hubify pod list --watch

# GPU utilization
hubify pod metrics pod-abc123

# Cost tracking
hubify pod cost --month current --breakdown
```

```
MONTH      TOTAL    H200    H100    A100    CPU
2026-04    $312     $180    $112    $20     $0
```

## Coming Soon: Modal

Modal integration will add serverless GPU functions. Instead of managing pods, you deploy functions that run on-demand and charge per second. Ideal for:

* Short-lived tasks (\< 10 minutes)
* Bursty workloads
* Figure generation
* Small inferences

Modal support is currently in development.