How much VRAM does ComfyUI actually need?
A workload-first guide to choosing 6, 8, 12, 16, or 24 GB for ComfyUI—covering offload, model precision, resolution, video, and the difference between fitting and working comfortably.
Bottom line
There is no single ComfyUI minimum. Buy for the largest model, resolution, and sequence you expect to use repeatedly—not for the smallest workflow you can make load once.
Decision table
| Hardware | Best fit | Editorial decision |
|---|---|---|
| 6 GB | Learning and constrained image workflows | Reasonable for older or smaller checkpoints at modest resolution. Expect frequent model unloading, conservative batches, and less room for large ControlNet or upscale chains. |
| 8 GB | Entry local image generation; selected offloaded video | A practical floor for many current image workflows. Official ComfyUI documentation also shows a Wan 2.2 TI2V 5B workflow designed to fit 8 GB with native offloading—proof that “loads” does not mean “fast” or “all video models fit.” |
| 12 GB | Comfortable enthusiast image workflows | More useful headroom for larger checkpoints, multiple conditioning models, and higher resolution. Still plan around offload for the heaviest diffusion and video graphs. |
| 16 GB | Complex image graphs and lighter video work | A strong general-purpose tier when you regularly combine a base model, ControlNet/IP-Adapter-style components, and upscaling. Sequence length can still dominate video memory. |
| 24 GB | Heavy diffusion, training experiments, and fewer compromises | The tier where large image pipelines and many video workflows become substantially less fragile. It is not unlimited: long clips, high resolution, and multiple resident models can still spill into system RAM. |
The number on a model card is not the whole workflow
VRAM must hold more than checkpoint weights. During execution, ComfyUI may also need a text encoder, VAE, intermediate activations, conditioning networks, attention state, and image or video tensors. Two graphs using the same checkpoint can therefore have very different peaks.
Treat “minimum VRAM” as the smallest documented envelope that can complete a specific workflow with specific settings. Treat “recommended VRAM” as the capacity that avoids constant offloading and lets you iterate without redesigning the graph around memory.
- Resolution increases tensor sizes; doubling width and height creates roughly four times as many pixels.
- Batch size and video frame count multiply work held or processed across the graph.
- ControlNet, adapters, upscalers, and a second model add their own memory pressure.
- Precision and quantization can reduce model memory, but may affect compatibility, quality, or speed.
Offloading changes feasibility, not physics
ComfyUI supports smart memory management, model unloading, reserved VRAM, CPU VAE execution, and low-VRAM modes in its server configuration. These controls can make a workflow complete on a smaller card by moving data between GPU and system memory.
The trade is latency and transfer overhead. If a graph repeatedly moves a large model over PCIe, it may be technically runnable but unpleasant for exploration. Keep adequate system RAM and fast storage available; VRAM pressure does not disappear when data is offloaded—it moves.
Video needs a workload description, not a blanket minimum
“AI video needs 24 GB” is too broad. The official ComfyUI Wan 2.2 documentation includes a 5B TI2V workflow intended to fit on 8 GB with native offloading. Larger Wan variants, higher resolution, longer sequences, and different precision choices create a very different requirement.
When comparing video claims, record the model variant, dimensions, number of frames, precision, attention implementation, and whether CPU offload is enabled. Without those details, a VRAM number is not transferable.
A repeatable way to choose a tier
Start with the output you expect to make weekly. Pick one representative graph, list every resident model, then leave headroom for preview, upscaling, and experimentation. If you must use aggressive offloading for the representative job, the card is a compatibility choice rather than a comfort choice.
For occasional use, accepting a slower offloaded workflow can be rational. For daily iteration, buying one tier above the demonstrated minimum usually improves the experience more than optimizing every node around a hard ceiling.
How this was researched
This guide synthesizes official ComfyUI system, server-configuration, and Wan workflow documentation. Editorial review checked every recommendation against the cited upstream material. We did not benchmark these tiers in a lab, so workload-specific fit is presented as planning guidance rather than measured performance.
Evidence confidence: High for principles; medium for workload-specific fit. “Documented” means an upstream source states the capability. Tier advice and scenario conclusions are editorial synthesis and should be validated against your exact model, settings, and runtime.
Read our complete methodology and corrections policy →Primary sources
- [1]
- [2]
- [3]Wan 2.2 video generation · ComfyUI Docs
Official workflow variants and the documented 8 GB TI2V 5B path.