Top Interview Questions & Answers: TensorRT, ONNX Runtime, Apache TVM in Model Compression & Quantization (2025)

Section 1: TensorRT – NVIDIA Deep Learning Inference Optimizer

1. What is TensorRT and why is it used?

Answer:
TensorRT is NVIDIA’s high-performance deep learning inference optimizer and runtime library. It is used to optimize, quantize, and accelerate deep learning models for deployment on NVIDIA GPUs, improving latency and throughput.

Queries: TensorRT inference optimization, NVIDIA model acceleration, deep learning deployment

2. What optimization techniques does TensorRT support?

Answer:

·         Layer and tensor fusion

·         FP16 and INT8 quantization

·         Kernel auto-tuning

·         Dynamic tensor memory

·         Precision calibration

Queries: TensorRT optimization techniques, TensorRT quantization, INT8 FP16 conversion

3. How does INT8 quantization work in TensorRT?

Answer:
INT8 quantization reduces the model's precision to 8-bit integers, improving inference speed and reducing memory usage. TensorRT uses calibration data to map FP32 activations to INT8 while preserving accuracy.

Queries: TensorRT INT8 quantization, INT8 calibration TensorRT, low-precision inference

4. What is the role of calibration cache in TensorRT?

Answer:
The calibration cache stores quantization scales for tensors from previous calibration runs, allowing re-use without re-running calibration.

Queries: TensorRT calibration cache, inference speedup, quantization reuse

5. How do you convert a PyTorch or TensorFlow model to TensorRT?

Answer:

1.  Convert the model to ONNX.

2.  Use TensorRT’s trtexec tool or APIs to convert ONNX to a TensorRT engine.

Queries: convert PyTorch to TensorRT, ONNX to TensorRT, model conversion pipeline

Section 2: ONNX Runtime – Cross-Platform Inference Engine

6. What is ONNX Runtime?

Answer:
ONNX Runtime is a high-performance inference engine for ONNX models. It supports multiple hardware accelerators and platforms like CPU, GPU, TensorRT, and DirectML.

Queries: ONNX Runtime inference engine, cross-platform model deployment, ONNX ecosystem

7. What are the benefits of using ONNX Runtime for model deployment?

Answer:

·         Platform-agnostic deployment

·         Hardware-accelerated backends (e.g., CUDA, TensorRT, OpenVINO)

·         Built-in support for quantization

·         Interoperability with multiple frameworks

Queries: ONNX Runtime advantages, ONNX inference, cross-framework deployment

8. How does ONNX Runtime support quantization?

Answer:
ONNX Runtime supports:

·         Post-training quantization

·         Dynamic quantization

·         Quantization-aware training (QAT)

Tooling: onnxruntime.quantization.quantize_dynamic() for dynamic quantization.

Queries: ONNX Runtime quantization, dynamic quantization ONNX, QAT ONNX

9. What is the difference between dynamic and static quantization in ONNX Runtime?

Answer:

·         Dynamic Quantization: Weights are quantized offline, activations are quantized on-the-fly.

·         Static Quantization: Both weights and activations are quantized using calibration data.

Queries: static vs dynamic quantization, ONNX quantization comparison

10. How do you optimize an ONNX model for runtime?

Answer:
Use onnxruntime.transformers.optimizer or ONNX Graph Optimization Tool:

·         Constant folding

·         Operator fusion

·         Redundant node elimination

Queries: optimize ONNX model, ONNX graph transformation, ONNX Runtime tools

Section 3: Apache TVM – Machine Learning Compiler Stack

11. What is Apache TVM?

Answer:
Apache TVM is an open-source deep learning compiler stack designed to optimize and deploy models on various hardware platforms. It performs model compilation, quantization, and kernel tuning.

Queries: Apache TVM compiler, deep learning deployment TVM, model tuning

12. How does Apache TVM perform model quantization?

Answer:
TVM supports:

·  Post-training quantization (PTQ)

·   Quantization-aware training (QAT)
It provides tools to reduce model precision while maintaining accuracy, and optimizes for CPU, GPU, and microcontrollers.

Queries: TVM model quantization, PTQ TVM, QAT TVM

13. What is Relay in Apache TVM?

Answer:
Relay is TVM's intermediate representation (IR) used to express and transform models during optimization and compilation phases.

Queries: TVM Relay IR, Apache TVM intermediate language, model transformation TVM

14. How is AutoTVM different from AutoScheduler in TVM?

Answer:

· AutoTVM: Manual template-based tuning.

· AutoScheduler: Template-free, automatically generates optimization strategies.

Queries: AutoTVM vs AutoScheduler, TVM tuning engines, model performance tuning

15. What hardware platforms are supported by Apache TVM?

Answer:

·         x86 CPUs

·         NVIDIA GPUs (CUDA)

·         ARM devices (Raspberry Pi, Android)

·         WebAssembly

·         Embedded devices (CMSIS-NN, microTVM)

Queries: Apache TVM supported hardware, model deployment embedded TVM

Conclusion

Model compression and quantization are critical for efficient deployment of AI models, especially in edge and real-time applications. Tools like TensorRT, ONNX Runtime, and Apache TVM play a vital role in achieving low-latency and low-footprint inference.