How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Technical Screen rounds at NVIDIA.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at NVIDIA during technical interviews.

Design and benchmark optimized inference pipelines

Quick Overview

This question evaluates knowledge of ML system design and model inference optimization, specifically familiarity with PyTorch's compilation stack (TorchDynamo, TorchInductor and external backends), common acceleration techniques such as quantization, operator fusion, CUDA graphs, batching and parallelism, and the competency to design fair, reproducible performance benchmarks. It is commonly asked to assess reasoning about performance trade-offs, measurement methodology and reproducibility when optimizing latency, throughput, GPU/SM utilization and memory, and it tests both conceptual understanding of compilation and optimization strategies and practical application in benchmark design and reporting.

Accelerating PyTorch Inference: TorchDynamo, Techniques, and Benchmark Design

Context

You are asked to explain how PyTorch's compilation stack accelerates inference and to design a fair, reproducible benchmark for measuring improvements over a vanilla PyTorch baseline.

Tasks

A) TorchDynamo (aka PyTorch Dynamo) for Inference

Describe:

What TorchDynamo does for accelerating inference.
How it captures and compiles graphs (graph breaks, guards, shape specialization).
How it relates to TorchInductor and to external backends (e.g., TensorRT).

B) Techniques to Speed Up Inference (briefly list and define)

Include, at minimum:

Data/model/pipeline parallelism
Effective batching
Operator fusion
Quantization
Kernel autotuning
CUDA Graphs
Overlapping compute and data transfer (pinned memory, streams)
Sparsity
Caching (e.g., KV-cache, allocator)
Graph-level compilers

C) Design a Fair Inference Benchmark

Specify:

Metrics: latency (p50/p95/p99), throughput, GPU/SM utilization, memory.
Test setup: GPU model, software versions, precision, batch size, sequence length, warmup and iteration counts, concurrency.
Baselines: vanilla PyTorch eager.
Reporting: absolute values and percentage improvements.