Training at Scale

Spreading one model across thousands of GPUs: data, tensor, and pipeline parallelism, FSDP/ZeRO sharding, activation checkpointing, and the interconnect that decides which of them you can afford.

Explainers

• Distributed Training, End to End — A 70-billion-parameter model needs 1.1 TB just to hold its weights, gradients, and Adam states — fourteen times what fits on one GPU. Compute the bytes, then install every fix in order: data parallelism with ring all-reduce, FSDP/ZeRO sharding, tensor and pipeline parallelism with their bubbles, activation checkpointing, and the interconnect that decides which of them you can afford — drawn, computed, and animated.