Deployment Overview | NVIDIA AITune Documentation

Deploy your AITune-tuned model with NVIDIA Dynamo. Choose a Python worker to serve your model or pipeline, or use Triton within Dynamo to serve an optimized artifact. Standalone Triton is also supported.

Deploy with Dynamo

Dynamo provides the serving frontend, service discovery, and request routing. There are two ways to connect your tuned model:

Serve a Python model or pipeline

Start here when you want to keep your existing Python inference code. Your application constructs the model and loads an AITune checkpoint. AITune’s Dynamo worker exposes your inference function through the OpenAI-compatible HTTP frontend. Start with FLUX image generation or WAN video generation; audio and embeddings are also supported.

Follow Deploy with Dynamo to serve FLUX on one GPU and generate your first image. Continue with its multi-GPU section for WAN video generation across multiple GPUs.

Serve a model through Triton within Dynamo

Use this path when you want Triton to execute the model behind Dynamo’s frontend and routing. AITune can generate a Triton repository from an ONNX Runtime, TensorRT, or TorchInductor AOT artifact, or publish an existing ONNX, TensorRT, or PT2 file with an explicit model configuration. Requests use the KServe gRPC tensor API.

Follow the Triton guide to prepare the repository, then choose Run Triton through Dynamo. That guide also explains how to use a checkpoint with Triton’s Python backend when your model needs Python code.

Alternative: standalone Triton

If you already deploy with Triton or want to serve directly from it, use the same model repository with standalone Triton. You can generate a repository for an optimized artifact or an existing model file, or provide a Python backend that loads an AITune checkpoint. All paths are covered in the Triton guide.

What to prepare

The serving platform and the saved model format are separate choices:

What runs inference	What you prepare	Where it can run
Python code that constructs your model or pipeline	An AITune checkpoint, model code, and dependencies	AITune’s Dynamo worker or Triton’s Python backend
Triton loading an optimized artifact or existing model file directly	A generated Triton model repository	Dynamo’s Triton worker or standalone Triton

The save and load guide applies to both Python deployment paths. Repository generation is covered in the Triton guide. Each deployment guide links to the preparation steps it needs.