How to Run AI Video Generation Locally (2026 GPU Guide)

Yes, you can run AI video generation locally. Open-weight models like Wan 2.2, LTX-Video, HunyuanVideo, CogVideoX and Mochi 1 run on your own GPU through ComfyUI or Hugging Face diffusers. The catch is VRAM: the smallest models fit in 5 to 8 GB, while the biggest need 45 to 80 GB of GPU memory.

This guide lists which models actually run locally, what their official repos say about hardware, and the four steps to your first clip. The second half covers where closed cloud models still win and how a local-first app like ViralMint fits around a local generator. Every VRAM figure below comes from the model’s own GitHub README or Hugging Face model card, linked inline.

Which AI video models run locally

Five open-weight model families have official weights and published hardware requirements you can run on your own machine. Closed models such as Sora 2 Pro and Veo 3.1 are not on this list because their weights are not released.

Two things the table does not show. First, “minimum VRAM” in a README usually assumes the model’s reference code; ComfyUI’s offloading and fp8 weights often lower it, at the cost of speed. Second, licenses differ: Apache 2.0 models are the simplest for commercial work, while the CogVideoX License, LTX-Video’s open weights license and Tencent’s community license each carry their own terms. Read them before you publish.

Hardware: what your GPU can realistically run

A 24 GB consumer card (RTX 4090 class) covers most of the practical local models; 8 to 16 GB cards run the small ones with offloading; the flagship open models want 45 to 80 GB data-center GPUs. Here are the tiers, built only from the official numbers above.

System RAM matters too once you rely on offloading: weights that don’t fit on the GPU are parked in regular memory, so a machine with little RAM can fail even when the VRAM number looks fine. The repos do not publish RAM minimums, so treat that as a general rule rather than a spec.

What about a Mac?

Apple Silicon Macs can run ComfyUI, but local video generation on a Mac is less predictable than on an NVIDIA GPU. ComfyUI’s README has an Apple Mac silicon section: install PyTorch nightly following Apple’s Accelerated PyTorch guide, then follow the normal manual install. The LTX-Video repo states that MPS “was tested with PyTorch 2.3.0”. Beyond that, the model repos publish requirements for NVIDIA GPUs only, and HunyuanVideo lists Linux as its tested OS. None of them give Mac memory figures, so we won’t invent any. Expect longer generation times and some workflows that simply won’t run.

How to run an AI video model locally (step by step)

Install ComfyUI, drop the model files into its models folders, load the model’s workflow template, and queue a prompt. Using Wan 2.2 as the example, since ComfyUI’s docs cover it in detail:

1. Install ComfyUI

The easiest path is the ComfyUI desktop app, which the README describes as “available on Windows & macOS” from comfy.org/download. On Linux, or if you prefer a manual install, the README’s steps are: clone the repo, install PyTorch for your GPU, install the requirements, and start the server.

git clone https://github.com/Comfy-Org/ComfyUI.git
cd ComfyUI
# NVIDIA: PyTorch stable build, per the ComfyUI README
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130
pip install -r requirements.txt
python main.py

The README notes that Python 3.13 is very well supported. Open the local address ComfyUI prints in the terminal to reach the interface.

2. Download the weights from Hugging Face

ComfyUI’s Wan 2.2 tutorial lists the exact files for the 5B model:

• wan2.2_ti2v_5B_fp16.safetensors goes in models/diffusion_models/
• wan2.2_vae.safetensors goes in models/vae/
• umt5_xxl_fp8_e4m3fn_scaled.safetensors goes in models/text_encoders/

The 14B text-to-video workflow instead uses a high-noise and a low-noise diffusion model plus wan_2.1_vae.safetensors. Other model families follow the same pattern: diffusion model, text encoder and VAE, each in its own folder.

3. Load a workflow

In a current ComfyUI, open the Template Library and search “Wan2.2” to load a ready-made text-to-video or image-to-video workflow. LTX-Video’s official nodes live in ComfyUI-LTXVideo, and HunyuanVideo-1.5 ships a ComfyUI integration guide in its repo.

4. Generate

Write your prompt, set the resolution and frame count (the length parameter on the latent node controls how many frames you get) and queue the workflow. The clip is written to ComfyUI’s output folder. Start short and low-resolution until you know how your GPU handles the model, then scale up.

If you prefer code to nodes, most of these models also have Hugging Face diffusers pipelines; the CogVideoX and Mochi 1 VRAM figures above were measured by their authors through diffusers.

Where cloud models still win

Sora 2 Pro and Veo 3.1 are closed-weight models: you cannot download them, so they cannot run locally at any price. They are only reachable through their vendors’ hosted APIs or apps that proxy them.

The trade-off with open-weight local models is straightforward:

• What you gain locally: prompts and frames stay on your machine, no per-clip fee once you own the hardware, no content queue, and full control over seeds, LoRAs and workflows.
• What you give up: the documented outputs in the table above sit around 480p to 720p and 5 to 10 seconds per clip, generation takes minutes per clip on consumer hardware, and the largest open models need GPUs most people don’t own.

We’re not going to rank them with made-up benchmark scores. The honest version: if you need a specific short shot and have the GPU, a local model is private and free per clip. If you need longer, higher-resolution footage with synced audio and no hardware, a hosted flagship model is the practical option.

The ViralMint approach: local-first around the model

ViralMint is a local-first desktop app: downloading, transcription, captioning, editing, rendering and voice cloning run on your machine, while the generative-AI steps are metered cloud calls. It does not run open-weight video generation models locally. Its AI video clips come from hosted models. Here is exactly where each step runs.

What leaves your machine

Your source videos, transcripts, rendered outputs and cloned-voice reference audio stay on your disk. Only the input a cloud step needs is sent: the brief for script generation, the script text for paid TTS, the prompt (and any starting image you picked) for AI image, music or video. There is no background sync of your library. ViralMint is open source under AGPL-3.0, so you can read the code path for every cloud call; see the open-source AI video toolset.

There is also no bring-your-own-key mode: provider keys stay on ViralMint’s backend and cloud steps are paid per action from a prepaid balance. That is a trade-off worth knowing if you want to route generation to your own account.

Pair a local model with ViralMint

A practical split if you already run ComfyUI:

1. Generate the shots locally. Use Wan 2.2, HunyuanVideo-1.5 or another open model for the specific clips you need.
2. Import them into Smart Video as your own clips. Smart Video’s clip import blends your footage with matched stock so the cut reads as one piece.
3. Let ViralMint finish the video. Script or your own audio, voiceover, word-by-word captions, background music and stitching are handled in the same pipeline, and you download the mp4 to post yourself.

Your local clips are processed on your machine like any other source media. Cloud calls happen only for the steps you choose, such as a paid TTS voice or a script draft.

Download ViralMint to finish locally generated clips into a captioned, voiced short.

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