← 🖥️ ML Infrastructure & ServingNEXT IN ML INFRASTRUCTURE & SERVINGMixed-Precision Training→
Core
Distributed Training: Parallelism and FSDP
Training large models needs many GPUs, and there are distinct ways to split the work: data parallelism replicates the model and splits the batch; FSDP/ZeRO shards the optimizer state, gradients, and parameters across GPUs to fit models that do not; tensor parallelism splits a layer's matrices within a node; pipeline parallelism splits layers across nodes. Communication is the scaling bottleneck. Applied-AI interviews probe it because 'this model does not fit on one GPU' has specific, named answers and trade-offs.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
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?→
COMPANIES THAT ASSUME THIS