ComfyUI local AI computer guide
How to run ComfyUI AI workflows locally with 8 GB VRAM
We tested ComfyUI with Wan 2.2 on a computer with 48 GB of system RAM and 8 GB of GPU memory. It worked. One affordable, high-RAM computer could handle a wide range of local video work without top of the line video cards.
Our test computer
48 GB system RAM
This is the computer's main memory. It gives ComfyUI room to move model parts away from the graphics card and keep large work files open.
8 GB GPU memory
This is the fast memory on the graphics card. We used an NVIDIA RTX 4060. It is limited in capacity, so ComfyUI must move data in and out while a large job runs.
Wan 2.2 video model
We used the open source Wan 2.2 image/text-to-video AI model to test a real video workflow.
What you can build on one local workstation
Images and style tests
Make pictures, change colors and clothing, build moodboards, or use small style add-ons called LoRAs. They are good for exploring a look, but not perfect for keeping one character exactly the same.
Video and cleanup
Create short clips, add frames for smoother motion, enlarge output, make masks to alter videos, remove backgrounds, and join several parts into one scene.
Batch media work
Resize, convert, or clean up many files.
Why high RAM matters for ComfyUI
The same computer can make images, turn images into short videos, enlarge pictures, create masks, cut out objects, add frames between video frames, join layers, and convert many files in a batch. It can also keep large models and temporary files ready for the next job.
You do not need a different computer or pay-by-use cloud models for every task, just one computer can be an all-purpose creative box.
How ComfyUI works
ComfyUI looks like a board of boxes joined by lines. Each box does one step. One may load a model. Another reads your words. Another makes an image. The last box saves the file.
The best simple description is: Photoshop node graph meets alchemy lab meets synthesizer rack. It is powerful, but it can feel strange at first. There are many knobs, model names, seeds, masks, samplers, and add-ons. A copied workflow may break after one missing file or update.
Much of ComfyUI is learned by trying a workflow, looking at the output, and changing one setting at a time.
How to start with Wan 2.2 on 8 GB VRAM
- Install or update ComfyUI from the official download page.
- Open Workflow, then Browse Templates, then Video.
- Choose the official Wan 2.2 5B video generation template. The 5B version is the sensible place to begin on an 8 GB card.
- Download the model files listed by the template and place them in the folders shown in the guide.
- Keep the starting size and frame count low. Write a short, clear request and run the queue. Write a *negative prompt* to block bad generation habits.
- If it works, change only one item at a time. Raise the frame count or picture size in small steps.
ComfyUI's official Wan 2.2 guide says the 5B model should fit well on 8 GB VRAM with its built-in memory offload. Your run time will change with the graphics card, output size, frame count, model files, and other software using memory.
How 8 GB reaches beyond 8 GB
When a full job does not fit in GPU memory, ComfyUI can keep some parts in system RAM and move them to the GPU when needed. This is often called offload. Think of the GPU as a small workbench and system RAM as the nearby shelf.
Other tools can also help:
- Smaller model files: reduced-size models use less memory. Smaller versions of big models are often made through quantization: it may reduce accuracy and detail in order to fit into your computer.
- Tiling: the computer works on small parts of an image instead of the whole image at once. Seams or added run time are possible.
- Queued renders: let slow jobs run one after another, often overnight.
- Graph changes: remove unneeded steps and unload a model after its part of the job is done.
- Small previews: test the idea at a low size before spending time on the final output.
This does not make an 8 GB card perform like a 24 GB card. It lets an 8 GB card try more workflows than its number suggests. The price is usually time, lower detail, smaller output, or a more careful setup.
Where local video still looks weak
Local image-to-video can produce stiff faces, repeated hand moves, flat voices, and people who start to look alike. Longer clips also give the model more time to drift away from the first image.
But a new idea, a clear story, and a short clip can matter more than perfect realism. The output is also private and easy to share.
Who should choose more RAM?
Choose a high-RAM machine when you want one local computer for many creative jobs, keep several large models, or plan to work with video and large temporary files. For our 8 GB GPU test, 48 GB of system RAM gave the workflow useful room. A 64 GB machine is an easier target to find and gives more space for future jobs.
Choose a larger GPU first when speed is the main goal, you need high-resolution video every day, or you do not want to tune low-memory workflows. More system RAM expands what can be attempted. More GPU memory usually makes those jobs easier and faster.
The main lesson
Do not judge a local AI workstation only by whether one model starts or how fast one sample finishes. Judge the whole workflow. A machine with plenty of system RAM and an 8 GB graphics card can move among image creation, video, cleanup, masking, and file conversion.
That broad workflow capacity is the real workstation advantage. The computer can keep gaining flexibility and capabilities as software improves.