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Explain optimization and tensor vs pipeline parallelism

NVIDIASoftware Engineer · Technical Screen
HardSoftware EngineerTechnical ScreenMachine Learning

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This question evaluates knowledge of deep learning optimization techniques (quantization, pruning, knowledge distillation, kernel/operator fusion, memory and throughput optimizations) and model parallelism strategies (tensor versus pipeline), measuring competency in performance engineering, scalability, communication patterns and system-level trade-offs for training and inference. It is commonly asked to assess an engineer's ability to reason about practical and conceptual trade-offs, collectives, bottlenecks and mitigations in distributed and resource-constrained environments, and it falls under the Machine Learning/Deep Learning domain requiring both conceptual understanding and practical application.

Describe common AI optimization techniques for training and inference, including goals, methods, and trade-offs (e.g., quantization, pruning, distillation, kernel fusion, memory and throughput optimizations). Then compare tensor parallelism and pipeline parallelism: how each works, communication patterns, when to use them, and typical performance bottlenecks.