Runpod

RunPod Cloud GPU

Run open-source AI models on cloud GPUs via RunPod serverless. Pay-per-second, no minimums.

Setup

# 1. Create account at https://runpod.io
# 2. Add API key to .env
echo "RUNPOD_API_KEY=your_key_here" >> .env

# 3. Deploy any tool with --setup
python tools/image_edit.py --setup
python tools/upscale.py --setup
python tools/dewatermark.py --setup
python tools/sadtalker.py --setup
python tools/qwen3_tts.py --setup

Each --setup command:

1. Creates a RunPod template from the Docker image
2. Creates a serverless endpoint with appropriate GPU
3. Saves the endpoint ID to .env (e.g. RUNPOD_QWEN_EDIT_ENDPOINT_ID)

Available Images

All images are public on GHCR — no authentication needed.

Total monthly cost: Rarely exceeds $10 even with heavy use.

How It Works

All tools follow the same pattern:

Local CLI → Upload input to cloud storage → RunPod API → Poll for result → Download output

1. File transfer: Tools use Cloudflare R2 when configured (R2_ACCOUNT_ID, R2_ACCESS_KEY_ID, R2_SECRET_ACCESS_KEY, R2_BUCKET_NAME), falling back to free upload services
2. RunPod API: Tools call the /run endpoint, then poll /status/{job_id} until complete
3. Cold vs warm start: First request after idle spins up a worker (30-90s). Subsequent requests are fast (5-15s)

Endpoint Management

Workers

workersMin: 0    — Scale to zero when idle (no cost)
workersMax: 1    — Max concurrent jobs (increase for throughput)
idleTimeout: 5   — Seconds before worker scales down

Across all endpoints, you share a total worker pool based on your RunPod plan. If you hit limits, reduce workersMax on endpoints you're not actively using.

Checking Endpoint Status

Each tool stores its endpoint ID in .env:

Disabling an Endpoint

To free worker slots without deleting the endpoint, set workersMax=0 via the RunPod dashboard or GraphQL API.

Troubleshooting

Force Image Pull

When you push a new Docker image version, RunPod may still use the cached old one. To force a pull:

1. Update the template's imageName to use @sha256:DIGEST notation
2. Wait for the worker to restart
3. Revert to :latest tag after confirming

Cold Start Too Slow

• qwen3-tts: ~70s cold start, ~7s warm
• sadtalker: ~60s cold start, ~10s warm
• image_edit: ~90s cold start, ~15s warm

If cold starts are a problem, set workersMin: 1 (costs money when idle).

Job Fails with OOM

The model needs more VRAM than the GPU provides. Options:

• Use a larger GPU tier
• For dewatermark: reduce --resize-ratio (default 0.5 for safety)
• For image_edit: reduce --steps

"No workers available"

You've hit your plan's concurrent worker limit. Either:

• Wait for a running job to finish
• Set workersMax=0 on endpoints you're not using
• Upgrade your RunPod plan

Docker Images

All Dockerfiles live in docker/runpod-*/. Images use runpod/pytorch as the base to share layers across tools.

Building for RunPod (from Apple Silicon Mac):

docker buildx build --platform linux/amd64 -t ghcr.io/conalmullan/video-toolkit-<name>:latest docker/runpod-<name>/
docker push ghcr.io/conalmullan/video-toolkit-<name>:latest

GHCR packages default to private — you must manually make them public for RunPod to pull them. Go to GitHub > Packages > Package Settings > Change Visibility.

Cost Optimization

• Keep workersMin: 0 on all endpoints (scale to zero)
• Only deploy endpoints you actively need
• Use workersMax=0 to disable idle endpoints without deleting them
• Qwen3-TTS is significantly cheaper than ElevenLabs for voiceovers
• Check the RunPod dashboard for usage and billing