Local or rented: where ComfyUI runs

The one decision that trips up newcomers is not a nodeNodeA single block in a visual pipeline like ComfyUI that does one operation and wires to others to form a workflow., it is where the thing runs. ComfyUIComfyUIThe dominant node-based interface for running diffusion models locally. Started by 'comfyanonymous' in January 2023, now stewarded by Comfy Org. needs a GPUGPU (Graphics Processing Unit)The parallel processor that runs image and video models. Its speed and memory largely determine what you can generate and how fast., and you either own one or you rent one. Owning is simplest and, past the hardware cost, free: no per-hour meter, nothing leaves your machine, and you can tinker endlessly. The moment your card runs out of memory, though, or you want to run a big video model, or you simply do not have a good GPU, you rent.

The rental world has three shapes. The first is a raw GPU pod, from a service like RunPod or the cheaper, auction-priced Vast: you spin up an instance, launch a one-click ComfyUI template, and reach the normal interface through your browser, paying by the second for as long as it runs. A consumer 4090 goes for well under a dollar an hour, a data-center H100 for a couple of dollars, the biggest cards for more, and you pick the GPU to fit the job. The second shape is serverlessServerlessRunning a model on infrastructure that spins up on demand and bills per use, so you do not manage an always-on GPU.: instead of a machine you keep running, you send a workflow to an endpoint that spins up, runs it, and scales back to zero, so you pay nothing while idle, which is how apps put ComfyUI behind an APIAPI (Application Programming Interface)The programmatic endpoint you call to run a hosted model from your own code instead of a web interface.. The third and newest shape is fully managed: Comfy Cloud, the makers' own hosted version, runs the whole thing for you on a large GPU with hundreds of models pre-loaded and no setup at all, billed by credits.

The trap in all three is persistence. A rented pod's disk is usually ephemeral, wiped when the pod stops, so your downloaded models and custom nodes vanish unless you put them on a persistent volume, and serverless carries a cold-start cost while a fresh worker loads models into memory. The rule of thumb: own a card if you tinker daily and care about privacy; rent a pod when you need more VRAMVRAM (Video RAM)The memory on a GPU. It sets a hard ceiling on the model size and image resolution you can run locally. than you have; go serverless when you are serving an app; and reach for the managed option when you want none of this to be your problem.