Introducing gigaGPT: GPT-3 sized models in 565 lines of code - Cerebras

Introduces gigaGPT, a 565-line implementation of nanoGPT that enables training GPT-3 sized models (up to 175B parameters) on Cerebras hardware. It leverages the hardware's large memory capacity (MemoryX) and compute power to stream model weights, eliminating the need for complex distributed training frameworks like Megatron or DeepSpeed. The post validates gigaGPT by training models of various sizes (111M, 13B, 70B, 175B) and highlights the simplicity of scaling training across 1 to 192 CS-2 systems using standard mini-batches (data parallelism). It also details the use of `cerebras_pytorch` for tracing compute graphs and managing distributed execution.