shootthesound/comfyui-mesh — explained in plain English
Analysis updated 2026-05-18
Run large models like FLUX.2 across two GPUs that individually lack enough memory
Pool a second GPU on your local network for faster generation
Split a video generation model like LTX 2.3 across two machines
Share a friend's GPU over a VPN connection for image generation
| shootthesound/comfyui-mesh | hamid-k/nginx-rift-private-lab | nvlabs/spatialclaw | |
|---|---|---|---|
| Stars | 67 | 67 | 67 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | hard |
| Complexity | 4/5 | 5/5 | 5/5 |
| Audience | developer | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires two Nvidia GPUs with NVENC support and a fast local network.
ComfyUI-Mesh is a tool for splitting large AI image and video generation models across two separate graphics cards (GPUs), either on the same computer or on two different machines connected over a local network. Normally, running a big model like FLUX.2 or LTX 2.3 requires a single very powerful GPU with enough memory to hold the entire model at once. This project solves that problem by dividing the model into a front half and a back half, each running on a different GPU. The clever part is how it handles the data transfer between the two cards. Modern Nvidia GPUs contain dedicated video compression chips called NVENC that normally sit idle during AI work. ComfyUI-Mesh uses those chips to compress the activation data (the intermediate results being passed between the two model halves) before sending it over the wire, much like compressing a video stream. This cuts the transfer size by three to ten times, making a regular gigabit home network fast enough for real-time use. The result is that FLUX.2 Klein 9B can generate a 1024-pixel image in roughly 4.4 seconds split across two cards over ethernet. The system has two parts: Icarus, a custom node installed inside ComfyUI (the popular AI image generation interface) on your main machine, and Daedalus, a server that runs on the second machine. You would use this if you have two Nvidia GPUs that individually do not have enough memory to run a large model on their own, or if you want to pool a friend's GPU over a VPN connection. It currently supports FLUX.2 Dev, FLUX.2 Klein 9B, and LTX 2.3 video models, with more architectures planned.
A tool that splits large AI image and video models across two GPUs, using video compression hardware to speed up data transfer between them over a network.
Mainly Python. The stack also includes Python, ComfyUI, CUDA.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.