The question "can I make AI video locally?" has two answers, because it is two questions. If you mean generating photoreal footage from a prompt, you need a discrete graphics card with a lot of memory, and there is no honest way around it. If you mean producing a finished, narrated, animated video on your own machine, you need no graphics card at all, and the tools have worked that way for years. Guides answer the first question and let readers conclude the second is impossible.
Can you run AI video generation locally?
Yes, if your machine has a discrete NVIDIA card with enough memory, and no if you are hoping to do it on a processor alone. Open video models are genuinely heavy. Tencent's HunyuanVideo repository states plainly that "an NVIDIA GPU with CUDA support is required", and recommends 80GB of memory for good quality.
What matters more is that this is only one of two production routes, and the industry has quietly defined the category so that the other one does not count. A typical guide opens by defining a local AI video generator as an open-weight model you run on your own GPU. That definition is not wrong, it is just narrow enough to exclude the answer most readers need.
Generative video invents pixels: photoreal footage, camera moves, imagined scenes. It runs a diffusion model and it is hungry. Composed video draws frames from things you specify: typography, charts, diagrams, motion graphics, narration over animation. It runs like software, on a processor, and a graphics card barely helps. Explainers, tutorials, product walkthroughs and data videos are all the second kind.
How much VRAM do you need for AI video generation?
Between roughly 5GB and 80GB, depending on the model, and the cheap end of that range buys you six seconds of 480p at 8 frames per second. Every figure below comes from the model's own repository or card, not from a listicle.
Reading those figures in order: CogVideoX-5B starts at 5GB, Wan 2.1's small model states 8.19GB, HunyuanVideo 1.5 states 14GB with offloading enabled, Wan 2.2's consumer model wants 24GB, HunyuanVideo needs 45GB at 544 by 960, Mochi 1 asks for about 60GB, and Wan 2.2's full model requires 80GB. For scale, roughly 7% of machines in the Steam hardware survey have 24GB or more, and that survey covers PC gamers, a group biased towards owning good graphics cards.
The low numbers come with small outputs. CogVideoX-5B runs from 5GB and produces 720 by 480 at 8 frames per second, capped at 6 seconds, with the model card noting no support for other resolutions. Wan 2.1's 8.19GB model makes 480p, and takes about four minutes for five seconds on an RTX 4090. The cheapest entry to generative video is not a cheap way to make a video.
Is 8GB of VRAM enough for AI video generation?
Technically yes, through offloading and quantization, and the cost is time. Community forks advertise running select models in as little as 6GB. What they rarely publish alongside is what that costs, and there is a measured answer.
The diffusers library documents the mechanism bluntly: CPU offloading "dramatically reduces memory usage, but it is also extremely slow because submodules are passed back and forth multiple times between devices", and can be "impractical due to how slow it is." A published benchmark on an image model puts a number on it: cutting GPU memory from 33.85GB to 2.41GB raised the generation time from 6.6 seconds to 68.4 seconds. That is 10.4 times slower for the memory saving.
That exchange rate is the fine print on every "runs in 6GB" claim. The model does fit. It just stops being a tool you would use twice.
What can you make locally with no graphics card at all?
Finished, narrated, animated video: explainers, tutorials, data stories, product walkthroughs, anything built from typography, charts and motion rather than from imagined footage. This route needs no graphics card, and it is not a workaround. It is how a large share of professional motion graphics is produced.
The clearest confirmation comes from Remotion, the most widely used programmatic video framework. Its own documentation states that "most renders do not become faster with GPU acceleration", and its cloud rendering products, which it describes as the fastest option, run on infrastructure with no GPU at all. There is a wrinkle worth knowing: in headless mode, which is how programmatic rendering runs, "Chromium disables the GPU" anyway. So even a machine with a good card largely does not use it for this work.
| Stage | What does the work | GPU needed? | Measured on a processor |
|---|---|---|---|
| Narration | A small speech model | No | 1.3x to 5x faster than real time |
| Composition | Code, drawn by a headless browser | No | 1 to 8 frames per second per core |
| Encoding | ffmpeg, H.264 | No | Median about 95 frames per second at 1080p |
| Generative footage | A diffusion model | Yes | No credible published measurement exists |
That last row is not an oversight. Searching the model repositories, issue trackers and benchmark datasets turns up no credible published measurement of an open video diffusion model generating a clip on a processor alone. The commonly repeated "many hours on CPU" figure traces back to a vendor blog that presents no benchmark. It is probably directionally right, and it is not evidence.
Does ComfyUI work without a GPU?
It installs and runs, and for video generation it is not a practical answer. ComfyUI has a CPU mode, and people do use it to confirm an installation works or to run very small image models. Applied to video, it inherits the same wall: a video model runs 3D attention across roughly 120 latent frames for 20 to 50 steps, against an image model's single frame for 1 to 2 steps.
For a sense of scale from a measured source, a benchmark of Stable Diffusion v1-4 on four processor cores took 49 seconds for a single image at just two denoising steps. A video model is eight to twenty-seven times larger in parameters, runs across many more frames, and needs ten to twenty-five times more steps. The direction of that arithmetic is not in doubt, though the exact figure remains unpublished.
How long does a local render actually take?
For composed video on an ordinary laptop, roughly one to two minutes of compute per minute of finished video. Each stage has been measured independently, and the numbers are unglamorous in a good way.
- 1Narration is faster than real time. Kokoro-82M measured about 5 times faster than real time on a 32-core cloud processor, and 1.3 to 1.8 times faster on a 4-core machine with no graphics card. A ten-minute narration takes two to eight minutes.
- 2Frame capture is the bottleneck. A Chromium benchmark measured 8 screenshots a second on a simple page and 1 a second on a complex one. At 30 frames per second, a 60-second video is 1,800 frames, so four to five minutes on a single core, divided by however many run in parallel.
- 3Encoding is not the problem. Across more than 2,000 public results of a standard x264 benchmark at 1080p, the median machine reaches about 95 frames per second and the slowest quarter still manage 42, which is faster than real time.
Two documented details save a lot of time. Writing frames as JPEG rather than PNG is markedly faster, and a Chromium engineer investigating capture speed found that "one of the larger contributing factors is PNG encoding". And blur, shadows and gradient backgrounds are the expensive effects, named directly in Remotion's performance guidance, because they are the ones that normally lean on a GPU that headless mode has switched off.
Can you generate AI video locally on a Mac, or on AMD?
For the composed route, yes on both, without qualification: it is ordinary software. For the generative route the picture is poorer, and most guides skip it entirely. HunyuanVideo requires CUDA and Linux. Community forks add AMD support for recent architectures. Apple Silicon has unified memory, which sidesteps the VRAM ceiling in principle, but published, reproducible timings for open video models on it are scarce.
Many people arrive here having run a local language model successfully and assume video will be similar. It is not. A text model streams tokens and degrades gracefully when it runs short of memory. A video model needs the whole latent sequence in memory at once, which is why the requirement is a cliff rather than a slope.
What can a machine with no graphics card genuinely not do?
Four things, and it is worth being precise about them rather than optimistic. It cannot generate photoreal footage. It cannot produce talking-head avatars, where one model requires 24GB even to run slowly. It cannot do WebGL-heavy 3D scenes at reasonable speed, since headless rendering disables the GPU regardless. And it will struggle with long renders at 4K, where pixel count dominates everything.
There is a neat symmetry in the limits, though. Stable Video Diffusion's own model card lists among its limitations that "the model cannot render legible text". Legible text is precisely what composed video does natively and effortlessly. The two routes fail at opposite things, which is the strongest practical argument for picking by output rather than by hardware.
AI Video & Voice-Over Skill Pack
Five agent skills that research, write, narrate, animate and package a video on your own machine. No GPU, no CUDA, no VRAM, and a permissively licensed local voice. Proven on a laptop with integrated graphics only.
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Sources
- Wan 2.2 repository and Wan 2.1 repository: the 24GB and 80GB requirements, 8.19GB for the 1.3B model, and the timing figures.
- HunyuanVideo repository and HunyuanVideo 1.5 model card: the CUDA requirement, the 45GB and 60GB memory table, and the 14GB offloaded minimum.
- Mochi 1 repository: approximately 60GB on a single GPU.
- CogVideoX-5B model card: from 5GB, capped at 720 by 480, 8 frames per second, 6 seconds.
- Stable Video Diffusion model card: the stated limitations, including that it cannot render legible text.
- Diffusers memory documentation and the group offloading benchmark: offloading is "extremely slow", and the measured 10.4 times slowdown.
- Remotion rendering comparison, GPU documentation and performance guidance: most renders do not benefit from a GPU, headless Chromium disables it, and which effects are expensive.
- Optimum benchmark CPU dataset: 49 seconds for one Stable Diffusion image on four cores at two steps.
- OpenBenchmarking x264 results: median about 95 frames per second at 1080p across more than 2,000 public results.
- Chromium headless discussion: 1 to 8 screenshots per second, and PNG encoding as a leading cost.
- Kokoro-82M model card, with CPU real-time factors measured in an independent benchmark.
- Steam Hardware and Software Survey, July 2026: the video memory distribution. Note the panel is self-selected PC gamers, so it overstates GPU availability among general users.
Related reading from Best Answer Hub: how to make videos with Claude Code, which free AI voice overs you can actually sell, and the free Best Answer Hub tools. The no-GPU pipeline lives at AI Agent Skills.