18 hours into fine-tuning a 70B model across 6 consumer GPUs and one node drops. Here's the brutal reality: most training frameworks handle this the worst way possible – they stall, rollback, or force a full restart.
This is the dirty secret of distributed training on consumer hardware. You're not running in a datacenter with redundant everything. When a node fails mid-training, traditional frameworks like DeepSpeed or FSDP don't have graceful degradation built in. They're designed assuming stable infrastructure.
The real question: can your framework checkpoint frequently enough that losing 18 hours doesn't mean losing 18 hours? And can it dynamically rebalance the model shards across remaining GPUs without starting over?
Most can't. That's why fault tolerance isn't just a nice-to-have for distributed training on consumer gear – it's the difference between a viable setup and an expensive experiment that fails at 3am.
This is the dirty secret of distributed training on consumer hardware. You're not running in a datacenter with redundant everything. When a node fails mid-training, traditional frameworks like DeepSpeed or FSDP don't have graceful degradation built in. They're designed assuming stable infrastructure.
The real question: can your framework checkpoint frequently enough that losing 18 hours doesn't mean losing 18 hours? And can it dynamically rebalance the model shards across remaining GPUs without starting over?
Most can't. That's why fault tolerance isn't just a nice-to-have for distributed training on consumer gear – it's the difference between a viable setup and an expensive experiment that fails at 3am.
