Can You Run AI Video Generation Locally on 8GB of VRAM?

AI video generators are quickly becoming the next big thing. Today, many powerful tools run in the cloud, so you don’t need high-end hardware to get impressive results. But what if you want to generate videos locally on your own machine, can you run it on just 8GB of VRAM? Spoiler alert: NO!!

You absolutely can’t run modern AI video generators with only 8GB of VRAM. Alright, blog over 😂. Not quite. In this post, we’ll break down why AI video generation is so resource-intensive and what makes it such a demanding task. We’ll also look at a tool toward the end that can sort of generate video even on a smaller GPU.

Why Are AI Video Generators Resource Intensive?

Temporal Consistency is Hard

Temporal Consistency refers to the ability of a video to remain stable and coherent over time, so that objects, people, lighting, and motion all stay believable from one frame to the next. It’s not just about generating a single high-quality image, but about producing a sequence of frames that feel like they belong to the same continuous moment.

Because of this added temporal structure, video models are significantly more complex than image models. They must balance spatial detail within each frame with consistency across frames, which is one of the main reasons AI video generation is computationally demanding and still an active area of research.

Models Are Huge

AI video models are large and resource-intensive, and for most people running them locally, the first limitation is often not performance, it’s memory. Many models are too large to fully load on lower-end GPUs, meaning users can run into VRAM limits before they even begin generating a video. Local workflows typically require around 12 GB to 24 GB of VRAM, while more complex setups or higher resolutions may need even more.

Disk space is another important factor. Because these models include large sets of learned weights and supporting components, a single setup can easily range from tens of gigabytes to over 100 GB of disk space.

Resolution And Quality Demands

Today, we’re used to watching high-quality videos with sharp detail and smooth motion, so it’s easy to assume AI video generators can produce the same level of output directly. In reality, achieving that level of quality, especially at higher resolutions like 1080p or 4K is still very computationally expensive, particularly on local hardware.

Maintaining consistency, detail, and smooth motion across frames becomes significantly more difficult as resolution increases. As a result, running high-quality AI video generation locally can quickly push hardware limits, especially when working with limited GPU memory and compute power.

Does That Mean Consumer PCs Are Hopeless?

It’s not hopeless to run AI video generation on a consumer PC, but it is definitely constrained compared to cloud-based systems. Large-scale cloud platforms rely on clusters of high-end GPUs and optimized infrastructure, which gives them a significant advantage in speed, resolution, and consistency. On a local machine, you can still use these tools, but you’ll need to work within tighter limits and adjust your expectations.

In practice, local AI video generation is most effective for short, low-to-moderate resolution clips rather than long, high-definition cinematic sequences. Performance depends heavily on your hardware, especially GPU memory, and even capable consumer setups may struggle with more demanding settings.

To make the most of a local system, users often rely on more efficient workflows. Keeping clip lengths short and resolutions moderate helps maintain both speed and consistency. Consider also image-to-video pipelines. They tend to be more stable and less resource-intensive than generating video purely from text prompts, since they start from a defined visual reference.

While results won’t match the quality or scale of large cloud systems, consumer hardware is still useful for experimentation, prototyping ideas, and exploring creative concepts.

Optimization Tips

Generate Small, Then Upscale

A common approach is to generate videos at a lower resolution and then upscale them afterward using dedicated enhancement tools. This reduces the load during generation, since resolution is one of the biggest factors affecting memory and compute requirements. Upscaling can improve visual sharpness, but it does not fully recreate lost fine detail or fix motion inconsistencies.

Short Clips + Stitching

Instead of generating long videos in a single pass, it is often more stable to produce short segments (for example, 2–5 seconds) and then combine them in a video editor. This helps reduce issues like motion drift or identity changes that can accumulate over longer generations.

Use FP16 (Half Precision)

Without going into too much technical detail, FP16, or half precision, allows models to use 16 bit floating point numbers instead of 32 bit ones. This reduces memory usage and can improve performance on modern GPUs without significantly affecting visual output in most cases.

Use Quantization

Quantization reduces the precision of a model’s stored weights to make it smaller and faster to run. This can significantly lower VRAM requirements, allowing larger models to run on consumer GPUs. However, more aggressive quantization levels can introduce visual artifacts or reduce temporal stability, so there is always a trade-off between efficiency and quality.

Use VRAM Offloading

When a model does not fully fit into GPU memory, parts of it can be temporarily moved to system RAM during generation. This makes it possible to run larger models on smaller GPUs, but it also introduces performance penalties because transferring data between RAM and VRAM is much slower than keeping everything on the GPU.

Generate GIFs

If you are seriously limited in GPU power, as a last resort, you can use GIF generation models instead of full fledged video generators. We will demonstrate one of these below. Unfortunately, GIF models usually struggle with temporal consistency and image quality, and they tend to fall short compared to modern video generation models. Still, if you only need simple animations and high image quality is not a priority, they can be a practical option.

What Can You Use?

Alright, enough talk. Let’s look at the tool that kind of works as we mentioned at the start. We will not go into installation details here, since that would require a tutorial of its own. However, the goal is to give you a taste of what is possible with limited resources.

For reference, we tested the tool on a 8GB RTX 3070 Ti.

AnimateDiff

AnimateDiff is a tool that lets you turn regular AI generated images into video. It uses a text to image generation model and adds a motion module on top of it to create animation.

There are different ways to use AnimateDiff. In our case, we used ComfyUI. If you are not familiar with ComfyUI, it is a tool for building and running AI image and video generation workflows using a visual node based system. Although there is a bit of a learning curve when working with nodes, it is highly recommended to learn it, as it is a very powerful and flexible tool.

If you want to learn more about the AnimateDiff ComfyUI integration, you can check out this link: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved. The good news is that installation can be done entirely through the ComfyUI user interface, so you can get it running fairly quickly.

Here Is a gif we generated of a cat probably writing a blog 😂:

Results

Generation Time: The GIF above took a little under 4 minutes to generate on 8GB VRAM. This is fairly good considering it consists of 24 frames at a resolution of 832×1216 using an SDXL model. Note that the animation shown above has been scaled down by a factor of 4 to optimize this web page. Generation took around 40 seconds using an SD 1.5 model to produce 24 frames at a resolution of 512×512.

Quality: motion is serviceable but can be inconsistent. Flickering appears in the video and temporal consistency can be better.

Disk Storage: only one extra model is needed for motion. Both the SDXL and version 1.5 models are under 2 GB in size.

Conclusion

Video generators are getting better and more powerful, but that also means most consumer PCs are not ideal for running them. Unless you have a very strong GPU with at least 32 GB of VRAM, performance will be heavily limited.

Storage is another major challenge. Video generation models can easily reach hundreds of gigabytes in size, which is difficult to accommodate on typical consumer systems. This is especially true today, when games and other applications already consume large amounts of disk space, and many SSDs still offer limited capacity. As a result, storage can fill up quickly.

However, we are still at the early stages of video generation technology. In the future, we may see more optimized approaches, such as quantized models and other efficiency improvements, that allow these systems to run much more smoothly on consumer hardware.

Alright, maybe your PC isn’t built for video generation, but there’s a good chance it can handle 3D mesh generation. Take a look at our blog to learn how to get started: The State of AI  3D Mesh Generators in 2026: Tools, Trends, and What’s Next.