The real bottleneck in distributed training on consumer GPUs isn't just compute—it's network latency vs. training time ratio.
Most frameworks do continuous synchronization after every training step. This means your GPUs sit idle waiting for gradient updates to propagate across nodes, completely wasting compute cycles.
The problem: network I/O and computation compete for resources instead of overlapping. Your RTX 4090 could be crunching the next batch while gradients sync, but instead it's blocking.
Smart async gradient aggregation or delayed parameter updates could keep GPUs saturated. Think ring-allreduce with pipelining or even stale gradient tolerance.
$RAVEN is apparently tackling this—worth checking if they're doing true async compute-communication overlap or just marketing distributed training as novel.
Most frameworks do continuous synchronization after every training step. This means your GPUs sit idle waiting for gradient updates to propagate across nodes, completely wasting compute cycles.
The problem: network I/O and computation compete for resources instead of overlapping. Your RTX 4090 could be crunching the next batch while gradients sync, but instead it's blocking.
Smart async gradient aggregation or delayed parameter updates could keep GPUs saturated. Think ring-allreduce with pipelining or even stale gradient tolerance.
$RAVEN is apparently tackling this—worth checking if they're doing true async compute-communication overlap or just marketing distributed training as novel.
