Explain optimization and tensor vs pipeline parallelism

Q: Explain optimization and tensor vs pipeline parallelism

This is a Machine Learning interview question from NVIDIA for Software Engineer roles. View the full question and solution on PracHub.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

Task: Deep Learning Optimization and Parallelism

You are asked to explain optimization techniques commonly used to improve deep learning training and inference. Address the following:

Part A: Optimization Techniques

Describe common AI optimization techniques for both training and inference. For each technique, state:

Goal(s)
How it works (at a high level)
Typical benefits
Trade-offs and pitfalls

Cover at least these categories and examples:

Quantization (e.g., INT8, FP8, PTQ vs QAT)
Pruning (unstructured vs structured, N:M sparsity)
Knowledge distillation (teacher–student)
Kernel/operator fusion (e.g., bias+GELU, FlashAttention)
Memory optimizations (e.g., activation checkpointing, sharding/offload, KV cache)
Throughput/latency optimizations (e.g., mixed precision, CUDA Graphs/compilation, batching, overlap of compute/comm)

Part B: Model Parallelism Comparison

Compare tensor parallelism and pipeline parallelism:

How each works
Communication patterns and collectives used
When to use each (practical scenarios)
Typical performance bottlenecks and how to mitigate them

Explain optimization and tensor vs pipeline parallelism

Task: Deep Learning Optimization and Parallelism

Part A: Optimization Techniques

Part B: Model Parallelism Comparison

Solution (Locked)

Comments (0)