Deploy DeepFloyd IF with BentoML: multi-stage GPU-aware serving on Kubernetes (Yatai)

AI Impact Summary

The content describes packaging the DeepFloyd IF multi-stage diffusion pipeline as a BentoML Bento, enabling per-stage Runners and GPU allocation, and deploying it via Docker/Kubernetes or Yatai with a Gradio UI for prompts. It leverages Hugging Face Hub for model access and BentoML Model Store for local management, detailing steps like importing models, defining a BentoML Service, and starting a server. This approach enables production-grade, scalable inference for a high-resource model, but requires substantial GPU memory and careful orchestration across stages to meet latency and cost targets.