Capability
Single-GPU Deployment & Cost
Deploy MaineCoon on a single GPU — 22B parameters, real-time inference, and generation costs below $0.001 per second.
Sample output
Text prompt to live character stream — audio and video generate together, chunk by chunk.
MaineCoon is designed for practical deployment, not just benchmark numbers. Single-GPU operation on H100 or RTX Pro 6000 makes real-time social AI economically viable for platforms, not just research labs.
Key highlights
Single-GPU real-time
Full 22B model runs on one H100 at 47.5 FPS or RTX Pro 6000 at 30+ FPS — no multi-GPU cluster required for inference.
Sub-cent per second
Generation cost stays under $0.001/s in typical conditions, dropping to $0.00025/s at full GPU utilization.
Efficient training too
Training completed in ~10k GPU-hours with precomputed features — making iteration feasible for a small team.
Metrics
How to verify
- Visit the official Experience Platform and input a text prompt
- Observe first-frame latency and continuous streaming output
- Try mid-stream prompt injection to test deployment behavior
FAQ
What GPU do I need to run MaineCoon?+
Official benchmarks use NVIDIA H100 (47.5 FPS) and RTX Pro 6000 (30+ FPS). Exact requirements for self-deployment depend on quantization and framework choices — check the official GitHub for updates.
How does cost compare to Veo 3?+
At full utilization, MaineCoon inference is approximately 1/2000 the per-second cost of Veo 3 in published comparisons — though direct comparison depends on use case and resolution.
Is MaineCoon open source?+
Catnip has published the technical report, model on Hugging Face, and GitHub repository. Check official channels for the latest licensing and deployment details.
Related capabilities
Experience MaineCoon live
Input a prompt and watch real-time streaming audio-visual generation on the official platform.