The common assumption is that consumer swarms are too slow due to latency. But my modeling suggests we are ignoring the "setup tax" of the cloud.
The Data:
- Cloud (AWS): For short, iterative runs (1-2 hours), you pay for nearly 45 minutes of dead time per session just setting up environments and downloading 140GB+ weights.
- Swarm (WAN): While inference/training speed is slower (1.6x wall clock time due to network latency), the environment is persistent.
The Trade off: The math shows that for iterative research, the swarm architecture becomes ~ 57% cheaper overall, even accounting for the slower speed. You are trading latency to bypass the startup overhead and the VRAM wall.
I'm trying to validate if this trade off makes sense for real world workflows. For those finetuning 70B+ models: Is time your #1 bottleneck, or would you accept a 1.6x slowdown to cut compute costs by half ?