Three core parallelism strategies dominate distributed training: data parallelism, model parallelism, and pipeline parallelism. Every major framework implements at least one.
But here's the catch: on consumer-grade hardware, they perform wildly differently. Not all parallelism is created equal when you're running outside datacenter-grade infrastructure.
The performance gap between these approaches on consumer GPUs is massive, and understanding which one fits your hardware constraints is critical for actual training efficiency.
Thread incoming on how each strategy behaves when you're not running on H100s 👇
But here's the catch: on consumer-grade hardware, they perform wildly differently. Not all parallelism is created equal when you're running outside datacenter-grade infrastructure.
The performance gap between these approaches on consumer GPUs is massive, and understanding which one fits your hardware constraints is critical for actual training efficiency.
Thread incoming on how each strategy behaves when you're not running on H100s 👇
