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Core
Distributed Training: Parallelism and FSDP
Training large models requires many GPUs, and the work can be split in distinct ways: data parallelism copies the model and divides the batch; FSDP/ZeRO shards the optimizer state, gradients, and parameters across GPUs to fit models that otherwise do not; tensor parallelism divides a layer's matrices within a node; pipeline parallelism divides layers across nodes. Communication is the scaling bottleneck. AI, ML, and GenAI engineer interviews probe it because 'this model does not fit on one GPU' has specific, named answers and trade-offs.
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PRACTICE THIS IN REAL QUESTIONS
ML Infrastructure & GPUsExplain data, tensor, and pipeline parallelism and FSDP/ZeRO, and size the memory for training a large model.→ML Infrastructure & GPUsWalk through the ZeRO stages and FSDP internals. Where does the memory actually go and when is it gathered?→ML Infrastructure & GPUsWhat are FSDP and DeepSpeed ZeRO, and how do their sharding stages differ?→ML Infrastructure & GPUsWhy are GPUs suited to deep learning, and how do GPUs, CPUs, and TPUs differ?→ML Infrastructure & GPUsYour GPUs sit at 40% utilization during training. How do you find and fix the bottleneck?→ML Infrastructure & GPUsYou doubled the GPUs but training barely got faster. Why doesn't distributed training scale linearly?→
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