Serving accelerated FLUX models on Modal (docs.thestage.ai)

TheStage AI published a hands-on tutorial and open-source code (ElasticModels) for serving its accelerated FLUX diffusion models on Modal with an OpenAI‑compatible API. This lets teams self‑host high‑performance image generation on Modal GPUs (L40s, H100, B200) using TheStage’s optimized runtime and Nvidia compiler, while exposing a familiar API surface for existing OpenAI clients. It’s aimed at production deployment—covering model acceleration, HTTP serving, endpoint setup, monitoring, logging, autoscaling and benchmark tooling—so organizations can match top API provider latency while retaining control over infrastructure and costs. Technically, the solution bundles TheStage AI’s prebuilt container images (public AWS ECR), a supervisor startup sequence that initializes the model server before Nginx to avoid cold-start errors, and Modal volumes to cache HuggingFace weights (first run single‑worker warmup, ~10–15 min; subsequent cold starts ≈60s). Example modal_serving.py shows image, env vars (MODEL_REPO, tokens, HF cache), GPU selection, 600 GB ephemeral disk and modal.web_server decorators. Multi‑GPU is supported via min_containers/max_containers and autoscaling (scaledown_window), and endpoints accept requests with an X‑Model‑Name header or via OpenAI client. Notes: anonymous ECR pulls capped at 500 GB/month, and commercial usage is free under a 4 GPU/hr average (licenses required above that). TheStage plans faster serialization, shorter cold starts and broader model coverage.