Local AI Video Generation Hardware Requirements: What Runs on 8GB, 12GB, 16GB and 24GB

The first question anyone asks before trying AI video at home is simple: will it run on my graphics card? The answers online are scattered across model repositories, forum threads and guesswork. This page puts them in one place. Every figure below comes from the official repository or documentation of each model, or from NVIDIA’s own guide, and the checker underneath matches your card to the models it can actually run.

Check what your GPU can run

How we compare: each requirement is the minimum published by the model’s own repository or documentation, read on 20 October 2026, or by NVIDIA for its tested workflow. Where a model lists several figures, we show the lowest one and the settings it assumes, such as offloading or 8-bit weights. We did not benchmark these models ourselves, so treat the figures as published floors rather than measured speeds.

Local AI video generation hardware requirements by GPU tier

Two numbers matter, not one. VRAM decides whether a model loads; system RAM decides whether offloading works. A 16GB card with 16GB of RAM will stall on models that run smoothly on the same card with 64GB, which is why NVIDIA’s video generation guide lists both.

Model-by-model requirements

Wan 2.2: the widest range

The Wan 2.2 repository says its 5B text-and-image-to-video model runs on a GPU with at least 24GB, such as an RTX 4090, producing 720p at 24fps. ComfyUI’s own Wan 2.2 documentation says the same model fits about 8GB with ComfyUI’s native offloading, which is why so many 8GB owners start here. The larger A14B models list 80GB with the official scripts; the repository’s offload options and community quantized builds bring them lower at the cost of speed.

HunyuanVideo 1.5: realistic people on 14GB

Tencent’s HunyuanVideo 1.5 repository lists 14GB as the minimum with model offloading enabled, for an 8.3-billion-parameter model that generates 121 frames at 480p or 720p. A 16GB card is the practical floor.

LTX: the only one with audio

Lightricks’ LTX models are 22-billion-parameter models that generate video and synchronized audio together. NVIDIA’s guide, built on LTX-2.3, lists 16GB of VRAM, recommends an RTX 5070 Ti or higher, and asks for 64GB of system RAM. The newer LTX-2.5 publishes no VRAM figure, but its repository suggests 8-bit casting and offloading when memory is tight, and the full download is roughly 66GB, so plan disk space too.

CogVideoX: the lowest floor

The CogVideo repository publishes the lowest figures here: 3.6GB for CogVideoX-2B and 4.4GB for CogVideoX-5B in 8-bit with every memory optimisation switched on, including CPU offload. The repository warns that memory use multiplies when those optimisations are off. Quality and resolution trail the newer models, but it is the realistic option on 4-6GB cards.

System RAM, storage and the rest of the build

System RAM: 32GB is the sensible minimum and 64GB is what NVIDIA lists for LTX. Storage: use an SSD with at least 100-200GB free; a single LTX download is about 66GB, and each model needs its own text encoder and VAE files. Operating system and drivers: NVIDIA’s guide targets Windows 11 with current drivers, and most community workflows are built and tested on NVIDIA cards. Power and cooling: video renders keep the GPU at full load for minutes at a time, so check the power supply and airflow before buying a bigger card.

If you are buying hardware for both video and chat models, our best local LLM guide covers the memory each language model needs, and the same card usually serves both. For a fully disconnected setup, see how to run AI models offline.

Which models to try first on your card

8GB: start with Wan 2.2 5B in ComfyUI at a modest resolution and short length, then try CogVideoX 1.5-5B in 8-bit. 12GB: the same models with longer clips and fewer crashes. 16GB: HunyuanVideo 1.5 for realistic people, and LTX if you also need audio and have 64GB of RAM. 24GB and up: run Wan 2.2 5B with its official scripts and use the headroom for higher resolution.

For which of these models produces the best results, rather than which ones fit, read our ranking of the best local AI video generator options and the best open source AI video generator guide. Our local AI guide covers the wider setup.

How one hobbyist matched a model to an 8GB card

Illustrative example. Tomas Varga, a hobby animator in Budapest, has an RTX 4060 with 8GB and 32GB of RAM. His first attempt with a 14B video model crashed with an out-of-memory error before rendering a single frame.

Using the tables above, he switched to Wan 2.2 5B in ComfyUI, which is documented to fit about 8GB with offloading, kept clips short at a modest resolution and closed his browser while rendering. The clips finished reliably, and he now knows that a 16GB card plus a RAM upgrade to 64GB is what would unlock HunyuanVideo and LTX, rather than a guess.

Frequently Asked Questions

What are the hardware requirements for local AI video generation?

At minimum, a graphics card with 8GB of VRAM and 32GB of system RAM for small models such as Wan 2.2 5B in ComfyUI or CogVideoX. HunyuanVideo 1.5 needs about 14GB of VRAM, LTX needs 16GB of VRAM and 64GB of RAM per NVIDIA, and larger Wan 2.2 models list 24-80GB with their official scripts.

What is the best GPU for local AI video generation?

For most people, a 16GB card such as an RTX 4060 Ti 16GB, RTX 5060 Ti 16GB or RTX 5070 Ti, because it runs HunyuanVideo 1.5 and LTX. A 24GB RTX 3090 or RTX 4090 adds headroom for higher resolution, and an RTX 5090 with 32GB is the top consumer option.

Can I generate AI video locally with 8GB of VRAM?

Yes. ComfyUI’s documentation says Wan 2.2 5B fits about 8GB with offloading, and CogVideoX models run in 8-bit from 4.4-7GB. Keep resolution and clip length modest, and have at least 32GB of system RAM so offloading works.

How much system RAM do I need for AI video generation?

32GB is the practical minimum because video models offload to system memory when VRAM runs out. NVIDIA lists 64GB for its LTX workflow, and 64GB is the safer choice if you want to run the larger models.

Can I run local AI video generation on a laptop?

Only the smallest models, and slowly. Laptop GPUs with 4-8GB can run CogVideoX in 8-bit or Wan 2.2 5B with offloading at low resolution. A desktop card with 16GB or more is far more practical for regular use.

Do AMD GPUs work for local AI video generation?

Some workflows run on AMD cards, but most official requirements, guides and community workflows are written and tested for NVIDIA GPUs. If you already own an AMD card, check each model’s repository and ComfyUI support before relying on it.

Conclusion

Local AI video generation hardware requirements come down to two numbers: VRAM to load the model and system RAM to offload it. 8GB with 32GB of RAM gets you started with Wan 2.2 5B and CogVideoX, 16GB with 64GB opens HunyuanVideo 1.5 and LTX, and 24GB or more removes most compromises.

Use the checker above before you buy or download anything. Match the model to the card you have, and upgrade only for the model you actually need.