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How to run large open-source models on a modest GPU with Comfy UI

Use Comfy UI's memory management and quantized model support to run bigger models on a small GPU.

  • Content
  • Intermediate
  • 120 minutes
  • From the README
  • Any agent

Authorization notice. Comfy UI is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forAn independent creator with a consumer GPU who wants to try large video or image models without buying new hardware.

The problemLarge diffusion models usually need more VRAM than a consumer card provides, so they fail to load or run very slowly.

The outcomeA large model running locally through Comfy UI with weight streaming and offloading, generating output on a small GPU.

What you need first

  • A Windows or macOS computer for the desktop app, or a Windows, Linux, or macOS machine with a supported GPU for the manual install
  • A GPU with enough VRAM for the model you pick (the README says large models can run on as little as 4GB VRAM with 8GB RAM)
  • Disk space for the model files you choose to download
  • Optional: a Comfy Cloud account or partner node access for paid hosted models

Steps

  1. Install Comfy UI using the Manual Install section or the desktop app, depending on your system.
  2. Download a quantized version of the model you want, since the README lists support for quantized models.
  3. Load the model with a workflow template from comfy.org/workflows that matches your model family.
  4. Queue the workflow with Ctrl + Enter and watch the GPU usage while it runs.
  5. If the run fails, lower the resolution or frame count and queue it again.

Prompt for your agent

Using the ComfyUI repo at https://github.com/Comfy-Org/ComfyUI, load a quantized version of <model name> on a machine with <GPU VRAM> VRAM and generate <output description>. Expected output: one generated file and a log showing the peak VRAM used. Verify the run finishes without an out-of-memory error.

Caveat. The README's claim of running the biggest models on 4GB VRAM plus 8GB RAM is the project's own statement, and actual speed and fit depend on the model and settings.

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