Capacity and parallelism
30%Senior signals
- ✓Estimates parameter, optimizer, activation, and communication memory.
- ✓Justifies data, tensor, pipeline, and sequence parallelism against topology and model shape.
Design the training stack for a 100B+ parameter model across tens of thousands of accelerators.
Design a platform to train a 100B+ parameter language model on tens of thousands of accelerators, where failures are expected during every run.
Use this as one discussion aid, not a single correct answer. Your design should follow from the requirements, scale, and trade-offs you establish.
graph LR
A["Design a Fault-Tolerant Distributed LLM Training Platform"]
A --> F1["Parallelism strategy and topology mapping"]
A --> F2["Input pipeline and data quality"]
A --> F3["Checkpointing, restart, and elastic recovery"]
A --> F4["Communication, stragglers, and performance efficiency"]
A --> F5["Numerical correctness, observability, and reproducibility"]This is an evaluation framework, not a single model answer. Strong designs may make different choices when their assumptions and trade-offs are explicit.
Practice drawing this system component-by-component on a live whiteboard while the interviewer probes at your target level.
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