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Cloud GPU processing via RunPod serverless.
Passed 21 of 21 test cases with the skill, and 13 of 21 without it.
and 7 more in the full report
8 fixed14 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
How long will a serverless job sit in the execution queue waiting for an available GPU before the system automatically cancels it to prevent endless billing retries?
| Checked | Without skill | With skill |
|---|---|---|
| States the automated queue timeout threshold is 300 seconds (5 minutes) | Missed | Passed |
The instructions your model reads when the skill loads.
Run open-source AI models on cloud GPUs via RunPod serverless. Pay-per-second, no minimums.
# 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
uv run tools/image_edit.py --setup
uv run tools/upscale.py --setup
uv run tools/dewatermark.py --setup
uv run tools/sadtalker.py --setup
uv run tools/qwen3_tts.py --setup
Each --setup command:
.env (e.g. RUNPOD_QWEN_EDIT_ENDPOINT_ID)All images are public on GHCR — no authentication needed.
| Tool | Docker Image | GPU | VRAM | Typical Cost |
|---|---|---|---|---|
| image_edit | ghcr.io/conalmullan/video-toolkit-qwen-edit:latest | A6000/L40S | 48GB+ | ~$0.05-0.15/job |
| upscale | ghcr.io/conalmullan/video-toolkit-realesrgan:latest | RTX 3090/4090 | 24GB | ~$0.01-0.05/job |
| dewatermark | ghcr.io/conalmullan/video-toolkit-propainter:latest | RTX 3090/4090 | 24GB | ~$0.05-0.30/job |
| sadtalker | ghcr.io/conalmullan/video-toolkit-sadtalker:latest | RTX 4090 | 24GB | ~$0.05-0.15/job |
| qwen3_tts | ghcr.io/conalmullan/video-toolkit-qwen3-tts:latest | ADA 24GB | 24GB | ~$0.01-0.05/job |
Total monthly cost: Rarely exceeds $10 even with heavy use.
test cases passed, vs 13 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.