Model Serving · Software component

Model Graph Exporter

Software componentModel ServingModelsarc:ModelGraphExporter

A model optimisation component that serialises a framework-specific model into a framework-neutral computation-graph format with declared dynamic input dimensions.

Responsibility. Decouples the model definition from its training framework so that quantizers and engine builders can consume it.

Also known as: ONNX export, Model export step

Direct neighbourhood (hover for relationship types)

Relationships

receives data from dynamic

Foundation LLM abstract Ch7.4 Ref7.01

produces lifecycle

Interchange Model Graph Ch7.4 Ref7.01

Design guidance

SHOULD declare batch and sequence dimensions as dynamic at export so the graph does not assume fixed input shapes.
SHOULD run export on hosts with memory of roughly twice the model size, or split the model into components.
MUST register or replace custom operators that lack interchange-format equivalents before export.

Quantitative guidance

As stated by the sources; verify before use.

Export of a 7B model takes 2-5 minutes and needs memory of roughly 2x model size (Ch7.4).

Classification

Patterns: Framework-agnostic intermediate representationDynamic axes (batch, sequence)
Technologies: ONNXPyTorch torch.onnx.exportTensorFlow
Quality attributes: Flexibility (ISO/IEC 25010)Compatibility (ISO/IEC 25010)
Risks mitigated: Engine inputs fixed to a single shapeFramework lock-in of the optimisation toolchain

Sources

Ch7.4: T. Nguyen, "TensorRT-LLM Fundamentals and Quantization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.4. ISBN: 9798244538229.
Ref7.01: NVIDIA, "Best practices," NVIDIA TensorRT Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html