I jumped straight into model parallelism and tensor sharding, which felt right, but I fumbled when they pushed on communication overhead between nodes.
Start by clarifying requirements and constraints, then propose a hybrid parallelism strategy (data, tensor, pipeline, and expert parallelism) tailored to trillion-parameter scale. Discuss memory optimization, communication overhead, and fault tolerance, and highlight how NVIDIA's hardware and software stack (e.g., DGX SuperPOD, NCCL, Megatron-LM) can be leveraged.
Pro tip: Emphasize the trade-offs between different parallelism dimensions and how you would dynamically adjust them based on model architecture and cluster topology. Mention specific NVIDIA tools like NCCL and Megatron-LM to show familiarity with the ecosystem.
Ask about model architecture, training data size, target hardware (e.g., NVIDIA GPUs), time-to-train, and budget. This ensures the design meets specific needs and shows you think before diving in.
Propose a combination of data, tensor, pipeline, and expert parallelism. Explain how each addresses memory and compute bottlenecks, and how they can be combined (e.g., 3D parallelism).
Discuss techniques like activation checkpointing, mixed precision, ZeRO optimizer, and efficient communication primitives (NCCL, NVLink, InfiniBand). Highlight how to overlap communication with computation.
Describe how to handle node failures, checkpointing, elastic training, and load balancing. Mention tools like NVIDIA Magnum IO and SHARP for scalable communication.
Conclude by explaining how NVIDIA's hardware (DGX SuperPOD, A100/H100) and software (Megatron-LM, NeMo) enable such a system, and summarize key trade-offs (e.g., communication vs. memory).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.