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What is comfyui-mesh?

shootthesound/comfyui-mesh — explained in plain English

Analysis updated 2026-05-18

67PythonAudience · developerComplexity · 4/5Setup · hard

In one sentence

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.

Mindmap

mindmap
  root((ComfyUI-Mesh))
    What it does
      Splits models across 2 GPUs
      Compresses transfer data
      Runs over LAN or same machine
    Tech stack
      Python
      ComfyUI
      NVENC
    Use cases
      Run FLUX.2 on two smaller GPUs
      Pool a friend's GPU over VPN
      Speed up large model inference
    Audience
      AI image generation users
      Multi GPU owners

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Run large models like FLUX.2 across two GPUs that individually lack enough memory

USE CASE 2

Pool a second GPU on your local network for faster generation

USE CASE 3

Split a video generation model like LTX 2.3 across two machines

USE CASE 4

Share a friend's GPU over a VPN connection for image generation

What is it built with?

PythonComfyUICUDANVENC

How does it compare?

shootthesound/comfyui-meshhamid-k/nginx-rift-private-labnvlabs/spatialclaw
Stars676767
LanguagePythonPythonPython
Setup difficultyhardhardhard
Complexity4/55/55/5
Audiencedeveloperresearcherresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires two Nvidia GPUs with NVENC support and a fast local network.

So what is it?

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.

Copy-paste prompts

Prompt 1
Help me set up ComfyUI-Mesh's Icarus node and Daedalus server across my two GPU machines.
Prompt 2
Explain how ComfyUI-Mesh uses NVENC to compress activation data between GPUs.
Prompt 3
Walk me through configuring ComfyUI-Mesh to split FLUX.2 Klein 9B across two Nvidia cards.
Prompt 4
Show me how to troubleshoot slow transfer speeds between the Icarus and Daedalus halves.

Frequently asked questions

What is comfyui-mesh?

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.

What language is comfyui-mesh written in?

Mainly Python. The stack also includes Python, ComfyUI, CUDA.

How hard is comfyui-mesh to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is comfyui-mesh for?

Mainly developer.

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