How to Run AI Video Generation Locally (2026 Guide)

If you want to know how to run AI video generation locally, the short version is this: you download an open-weight video model, drop it into a tool like ComfyUI, and generate clips on your own GPU — offline, with no per-clip fee and no rate limits. The long version depends entirely on your graphics card, because video is far hungrier than images.

This guide covers which models actually run on consumer hardware, how much VRAM each tier needs, how to set it up, and where a local AI video generator wins or loses against a cloud studio. No hype, just the trade-offs.

What "running locally" actually means

A local AI video generator is an open-weight model whose files you download and run on your own machine. Nothing leaves your computer. Compare that to cloud tools, where you send a prompt to someone else's servers and pay per generation.

The appeal is real: full privacy, no subscription metering, no content filters beyond your own, and unlimited iteration once the hardware is paid for. The catch is also real — you are now the person responsible for drivers, VRAM limits, and multi-minute render times. If you are new to how any of this works under the hood, our plain-English explainer on how text-to-video AI works is a good primer before you commit.

Which models run locally

A handful of open-weight models dominate offline AI video generation in 2026. The popular picks:

• Wan 2.2 — strong general-purpose quality, with permissively licensed variants that are safer for commercial work.
• LTX-Video — built for speed and low VRAM, with distilled versions that run on modest cards.
• HunyuanVideo — well-regarded for realistic human faces and motion.
• CogVideoX — comes in small (2B) and larger (5B) sizes, so the tiny version fits cramped GPUs.
• Mochi 1 — a high-quality open model, but heavier on memory.

All of these are downloaded from Hugging Face, which hosts the weights and model cards. For a broader tour of the landscape — including the closed cloud models you cannot run at home — see our guide to AI video generation models.

Hardware: what your GPU can realistically run

VRAM is the gatekeeper. Everything else — CPU, RAM, SSD — matters far less than how much memory your graphics card has. Rough tiers:

• 5-8 GB: the smallest distilled models (CogVideoX-2B, trimmed LTX builds). Short, low-resolution clips. Expect to wait.
• 10-16 GB: the practical sweet spot. Most mid-range models produce usable clips here, and this is where the majority of hobbyists land.
• 20-24 GB: comfortable headroom for higher resolution and longer sequences on a single card.
• 45-80 GB: the flagship, full-size models. This is data-center or workstation territory, not a typical home PC.

Beyond the GPU, aim for at least 16 GB of system RAM, a recent multi-core CPU, and a fast SSD with plenty of free space — individual models can be tens of gigabytes. If you want to understand the terms your card's spec sheet throws around, video memory (VRAM) is the one to learn first.

How to run an AI video model locally, step by step

The most common path uses ComfyUI, a free node-based interface for running these models. The flow is roughly the same no matter which model you pick:

• Install the runner. Download the ComfyUI desktop app for Windows or macOS, or use a one-click installer like Pinokio that handles Python environments and dependencies for you.
• Download the weights. Grab your chosen model's files from Hugging Face and place them in the correct ComfyUI model folders (the model card tells you which).
• Load a workflow. Most models ship an example workflow template. Open it in ComfyUI so the nodes are wired correctly instead of building the graph by hand.
• Write your prompt and queue it. Type your scene description, set resolution and clip length, and hit queue. Then wait — a few seconds to several minutes per clip depending on your card.

Your prompt quality matters as much offline as it does online. The same habits that get good results anywhere apply here, so it is worth reading how to write AI video prompts that actually work before you burn an hour on vague instructions.

Where local wins, and where it doesn't

Generating AI video on your own computer is genuinely great for privacy, tinkering, and avoiding per-clip costs once you own the hardware. If you render constantly, offline AI video generation can pay for itself.

But be honest about the ceiling. Local models generally trail the best cloud systems on resolution, maximum clip length, and sheer convenience. You also inherit every headache: driver conflicts, out-of-memory errors, slow renders, and the fact that keeping characters consistent across scenes is still hard to orchestrate by hand. And the upfront GPU cost is real — our breakdown of AI video generation cost puts the hardware-versus-subscription math side by side.

When a chat-directed studio makes more sense

For plenty of creators, the goal isn't to babysit a GPU — it's to finish a film. That's the gap Scriptly fills. Instead of installing models and wiring nodes, you direct an AI by chatting: design characters that stay consistent across scenes, plan storyboards, generate clips with selectable takes, and render a finished film with voiceover, word-synced captions, and music. It runs in your browser with nothing to install.

The practical move is to match the tool to the job. If you love the craft, want full offline control, and already own a capable card, a local AI video generator is a rewarding rabbit hole. If you'd rather spend your time on story and shots than on VRAM errors, a hosted studio gets you there faster.

Ready to make something instead of configuring something? Start creating with Scriptly and go from idea to finished short in a single conversation — or keep browsing the guides for more on prompting, characters, and storyboarding.