Skill Guide

ONNX export and TensorRT / vLLM inference optimization

The process of converting trained machine learning models into the interoperable ONNX format and deploying them for high-throughput, low-latency inference using NVIDIA TensorRT for GPU optimization or vLLM for efficient Large Language Model (LLM) serving.

This skill directly reduces cloud inference costs and improves application responsiveness, enabling scalable AI products. It is critical for organizations deploying ML at scale, where marginal gains in throughput translate to significant operational savings and competitive advantages in user experience.

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How to Learn ONNX export and TensorRT / vLLM inference optimization

1. Master the fundamentals of model graph formats (PyTorch/TF to ONNX) using torch.onnx.export or tf2onnx. 2. Understand core TensorRT concepts: layers, plugins, and precision calibration (FP32/FP16/INT8). 3. Learn the basic vLLM serving architecture, focusing on its PagedAttention mechanism for memory-efficient LLM serving.

1. Apply dynamic axes and custom ops during ONNX export to handle variable input shapes. 2. Profile and debug TensorRT engine performance using NVIDIA Nsight Systems and polygraphy. 3. Implement and benchmark continuous batching and KV cache optimization strategies in vLLM. Common mistake: Ignoring operator compatibility issues between frameworks.

1. Design and implement custom TensorRT plugins for unsupported or novel model operations. 2. Architect a unified serving pipeline that leverages TensorRT-LLM for non-LLM components and vLLM for generative models. 3. Establish performance SLOs (latency, throughput) and implement automated regression testing for model deployment pipelines.

Practice Projects

Beginner

Project

Export a ResNet-50 to ONNX and Infer with TensorRT

Scenario

Deploy a standard image classification model for a web service with strict latency requirements.

How to Execute

1. Use torchvision to load a pre-trained ResNet-50 model. 2. Export it to ONNX with torch.onnx.export, defining dummy inputs and output names. 3. Use the trtexec command-line tool to convert the ONNX model to a TensorRT FP16 engine. 4. Write a Python script using the TensorRT runtime to perform inference on a sample image.

Intermediate

Project

Optimize a BERT Model for TensorRT with INT8 Calibration

Scenario

Achieve maximum throughput for a sentiment analysis API serving a fine-tuned BERT model on an A10 GPU.

How to Execute

1. Export the fine-tuned BERT model from Hugging Face to ONNX, handling dynamic sequence lengths. 2. Prepare a calibration dataset and create an INT8 calibration cache. 3. Build an INT8 TensorRT engine with the calibration cache, polygraphy for validation, and layer-wise profiling enabled. 4. Deploy the engine in a simple Flask/FastAPI server and benchmark QPS against the PyTorch baseline.

Advanced

Project

Deploy a LLaMA-2 Model with vLLM and Implement Load Testing

Scenario

Serve a 7B parameter LLM for a high-traffic chatbot application, ensuring stable response times under concurrent user load.

How to Execute

1. Deploy the LLaMA-2-7B-chat model using vLLM's OpenAI-compatible API server. 2. Configure key parameters: tensor parallelism, GPU memory utilization, and max sequence length. 3. Use a load testing tool (e.g., Locust) to simulate 100 concurrent users sending prompts of varying lengths. 4. Monitor GPU memory, time-to-first-token (TTFT), and inter-token latency (ITL), then tune vLLM's continuous batching parameters to meet SLOs.

Tools & Frameworks

Export & Conversion

torch.onnx.exporttf2onnxONNX SimplifierONNX GraphSurgeon

Used in the initial model translation and graph cleanup phase. torch.onnx.export is the primary tool for PyTorch models. ONNX Simplifier and GraphSurgeon are essential for post-export graph optimization and surgery.

GPU Inference Engines

NVIDIA TensorRTTensorRT-LLMtrtexecpolygraphy

TensorRT is the core framework for optimizing and running GPU inference. trtexec is the CLI for benchmarking and building engines. polygraphy is for validation and debugging. TensorRT-LLM is the specialized library for LLMs.

LLM Serving Frameworks

vLLMText Generation Inference (TGI)NVIDIA Triton Inference Server

vLLM provides state-of-the-art throughput for LLM serving via PagedAttention. TGI is another popular alternative. Triton is the production-grade platform that can orchestrate TensorRT engines and other backends.

Profiling & Monitoring

NVIDIA Nsight SystemsNsight ComputePyTorch Profiler

Nsight Systems provides system-wide GPU/CPU timeline analysis. Nsight Compute offers detailed kernel-level profiling. These are non-negotiable for diagnosing bottlenecks in the inference pipeline.

Interview Questions

Answer Strategy

Structure the answer in clear phases: 1) ONNX Export (handling dynamic axes, custom ops), 2) TensorRT Parsing & Optimization (layer fusion, precision selection, calibration for INT8), 3) Engine Build & Profiling (using trtexec, analyzing layer timings). Highlight challenges like unsupported operators and the performance-accuracy trade-off in quantization.

Answer Strategy

The interviewer is testing understanding of memory management innovation. Explain the problem of memory fragmentation with standard key-value (KV) cache management, then describe PagedAttention's solution using virtual memory-like blocks. Emphasize the outcome: higher GPU memory utilization, enabling larger batch sizes and therefore higher throughput.