Most distributed training frameworks are built on a fantasy: perfectly uniform hardware. Same GPU model, same VRAM, same network bandwidth everywhere.
Real-world consumer hardware is chaos. You've got mixed GPU generations (RTX 3060 next to a 4090), wildly different RAM configs, CPUs thrown into the mix alongside GPUs. Standard frameworks like PyTorch DDP or Horovod just crash before training even begins.
The core issue: these frameworks assume homogeneous compute and symmetric communication patterns. They can't handle heterogeneous topologies where one node has 24GB VRAM and another has 8GB, or where network latency varies 10x between nodes.
This is why decentralized training on consumer hardware remains mostly theoretical. You need dynamic workload partitioning, adaptive batch sizing per node, and fault-tolerant gradient aggregation that doesn't assume everyone finishes at the same time.
Real-world consumer hardware is chaos. You've got mixed GPU generations (RTX 3060 next to a 4090), wildly different RAM configs, CPUs thrown into the mix alongside GPUs. Standard frameworks like PyTorch DDP or Horovod just crash before training even begins.
The core issue: these frameworks assume homogeneous compute and symmetric communication patterns. They can't handle heterogeneous topologies where one node has 24GB VRAM and another has 8GB, or where network latency varies 10x between nodes.
This is why decentralized training on consumer hardware remains mostly theoretical. You need dynamic workload partitioning, adaptive batch sizing per node, and fault-tolerant gradient aggregation that doesn't assume everyone finishes at the same time.
