Go repo explains multi-GPU training methods

Training large machine learning models across multiple GPUs is a distributed systems challenge driven by memory and compute walls. The author explains the foundational techniques used to address these constraints, including data parallelism with ring all-reduce, ZeRO parameter sharding, tensor parallelism, pipeline parallelism, and asynchronous checkpointing. Open article →