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 ?
aikitty•1mo ago
Have you looked at gpu marketplaces like io.net that offer much cheaper instances than AWS. You get both benefits: no setup tax between runs and lower costs. The trade off is you may be paying during idle time between experiments. But if you’re iterating frequently the math should still work out heavily in your favor.
Curious if you’ve modelled that vs your distributed swarm approach. It might be an easier path to cost and time savings without having to architect the distributed setup yourself.
miyamotomusashi•1mo ago
The Problem: To run a 70B model, you need around 140GB of VRAM.
On io.net/Vast: You can't find a single cheap consumer card with that memory (RTX 4090s cap at 24GB ). You are forced to rent expensive enterprise chips (A100s) or manually orchestrate a multi-node cluster yourself, which brings the DevOps headache.
On the Swarm: We handle that multi-node orchestration automatically. We stitch together 6x cheap 4090s to create one "Virtual GPU" with enough VRAM.
So if your model fits on one card, io.net wins. If it doesn't (like 70B+ models), that's where the swarm architecture becomes necessary.