Is 8 GB VRAM enough for AI video?

Yes, for images and LTX-Video motion, at roughly 2x the render time of a 16 GB card. FLUX wants 12 GB to be comfortable and Wan 2.2 will not run well at all. Expect tiled VAE and low-VRAM mode.

Hardware guide

What GPU do you need for AI video?

Q: How much VRAM do you need for AI video generation?

8 GB is the realistic floor for local AI video: it runs SDXL and FLUX images plus LTX-Video motion. 12 GB adds Wan 2.2. 16 GB is the comfortable sweet spot. Under 8 GB you can still make videos, but with stock footage or free cloud images instead of local AI.

Q: Can I make AI videos without a GPU?

Yes. Stock footage, free cloud image generation and code-drawn cards need no GPU at all and render a 10-minute video in 1-3 minutes. You only need a GPU for local AI images or real generated motion.

Q: Do I need a 24 GB GPU like a 4090?

No. 24 GB only meaningfully unlocks the heavy 14B models and removes offloading on Wan 2.2. For everyday faceless video, 12-16 GB with LTX-Video is the practical sweet spot and roughly 14x faster than Wan anyway.

Not "more is better" — here is exactly which capability each VRAM tier buys you, using the real minimums we gate on in production.

Thresholds from ReelForge's shipping hardware profiler · reference card: RTX 5060 Ti 16 GB

Short answer: 0 GB — you can still make videos (stock / free cloud images). 8 GB — the real floor for local AI: images + LTX motion. 12 GB — adds Wan 2.2 and comfortable FLUX. 16 GB — the sweet spot. 24 GB — only for the heavy 14B models.

What each tier unlocks

These are the exact bands our app uses to decide what to allow, whether to supersample, and which memory mode to launch the backend in.

Your VRAM	Local images (SDXL)	FLUX	LTX motion	Wan 2.2	Memory mode
No CUDA GPU	no	no	no	no	cpu
Under 8 GB	yes	no	tight	no	lowvram + tiled VAE
8 – 12 GB	yes	yes	yes	no	normalvram + tiled VAE
12 – 24 GB	yes	yes	yes	offloads	normalvram
24 GB+	yes	yes	yes	yes	highvram

Real model requirements

VRAM minimums and the disk you actually need to download. Note the companion files — LTX and Wan are not single downloads, which is the mistake most guides make.

Model	What it does	Min VRAM	Recommended	Download
SDXL	Images	6 GB	8 GB	6.5 GB
DreamShaper 8 (SD1.5)	Images, motion base	4 GB	8 GB	2.1 GB
AnimateDiff	Stylized motion	6 GB	10 GB	1.6 GB^*
LTX-Video	Real motion	8 GB	12 GB	10.7 GB^†
FLUX.1	Best local images	12 GB	16 GB	16.1 GB
Wan 2.2 (5B)	Cinematic motion	12 GB	20 GB	16.9 GB^‡
Wan 2.2 (14B MoE)	Heavy	20 GB	24 GB	26.8 GB

^* plus an SD1.5 base (DreamShaper) — AnimateDiff is a motion module, not a standalone model.
^† LTX 5.9 GB + a T5 text encoder 4.8 GB. The checkpoint alone will not run.
^‡ Wan 9.3 GB + umT5 encoder 6.3 GB + VAE 1.3 GB.

The offloading cliff nobody mentions

VRAM is not a soft limit — it is a cliff. While the model fits, you get the speeds in our benchmark. The moment it does not, weights spill into system RAM over PCIe and throughput collapses far worse than any "×1.3 slower" rule suggests.

Live example: Wan 2.2's 5B checkpoint plus its umT5 encoder is roughly 16 GB — the entire VRAM of our reference card. It "runs" on 16 GB, but it is already offloading, which is exactly why it measures 332 seconds per scene instead of the tens of seconds the model size would suggest. Its recommended tier is 20 GB for that reason.

This is why our tables list minimum and recommended separately, and why we gate Wan off below 12 GB entirely rather than let it technically start and then crawl.

So what should you actually buy?

Nothing, to start. Stock footage and free cloud image generation need no GPU and produce a real, publishable 10-minute video in 1–3 minutes. Prove the workflow first.
8 GB (e.g. 3060 Ti / 4060) — the honest entry point for local AI. Images, plus LTX motion at roughly 2× our reference times. Tiled VAE, low-VRAM mode, no supersampling.
12–16 GB (e.g. 4060 Ti 16 GB / 5060 Ti 16 GB) — the sweet spot. Everything except comfortable Wan. This is our reference card and it renders a 10-minute LTX video in 16 minutes.
24 GB (e.g. 4090 / 5090) — only worth it if you specifically want Wan 2.2 or the 14B models without offloading. For everyday faceless video it buys you roughly a 1.4× speedup on a model you probably should not be using daily anyway.

The unintuitive part: spending 3× more on a 24 GB card mostly unlocks the slowest engine. LTX on a 16 GB card beats Wan on a 4090 for throughput — by a lot. Buy VRAM for the models you will actually run daily, not the ones you will run twice.

Non-NVIDIA and laptops

The local AI stack here is CUDA-only in practice — AMD and Intel GPUs fall back to the keyless styles (stock footage, free cloud images), which is a perfectly good workflow, just not local AI. One exception: our frame-interpolation step uses a Vulkan binary and runs on any GPU. On Blackwell cards (RTX 50xx, sm_120) you need a CUDA 12.8 build of torch; older builds will not start at all.

FAQ

How much VRAM do you need for AI video generation?

8 GB is the realistic floor for local AI (images + LTX motion). 12 GB adds Wan 2.2. 16 GB is comfortable. Under 8 GB you can still publish — with stock footage or free cloud images.

Can I make AI videos without a GPU?

Yes. Stock footage, free cloud images and code-drawn cards need no GPU and render a 10-minute video in 1–3 minutes.

Is 8 GB VRAM enough?

For images and LTX motion, yes — at about 2× the render time of a 16 GB card, with tiled VAE and low-VRAM mode. FLUX wants 12 GB; Wan 2.2 will not run properly.

Do I need a 24 GB GPU like a 4090?

No. 24 GB mainly unlocks the heavy 14B models and removes Wan's offloading. LTX on 16 GB is ~14× faster than Wan regardless of card.

Why does LTX need two downloads?

The LTX checkpoint ships without a text encoder. It needs a separate T5 encoder (4.8 GB) to run — 10.7 GB total. Wan needs three files (checkpoint + umT5 + VAE, 16.9 GB).

Measured render timesSeconds per scene for every engine, on a real card What a faceless channel really costsHardware, electricity and the credit bill

Not sure what your PC can run?

ReelForge detects your GPU and tells you which styles it can handle — and how long each will take — before you download anything.

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