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Video Generation VRAM Requirements 2026: Every AI Video Model GPU Guide

Complete GPU and VRAM requirements for AI video generation in 2026. FP16 vs FP8 vs GGUF comparison for LTX Video, Wan Video, HunyuanVideo, CogVideoX, Mochi 1, and AnimateDiff. What you can actually run on 8GB, 12GB, 24GB, and 48GB GPUs.

The VRAM numbers you see for AI video models are terrifying. HunyuanVideo at 47-58 GB. Wan Video 14B at 54-65 GB. These FP16 figures make video generation look like a datacenter-only activity.

They are also deeply misleading for anyone considering local video generation in 2026.

With FP8 quantization, GGUF weights, and tiling strategies, nearly every major video model now runs on consumer GPUs. HunyuanVideo drops from 47 GB to 8 GB. Wan Video 14B goes from 54 GB to 6 GB at 480p with GGUF. This guide covers the real VRAM requirements for every open video model in 2026, and what you can actually generate on your hardware.

FP16 vs FP8 vs GGUF: The Numbers That Matter

The gap between theoretical and practical VRAM is larger for video models than any other category. Here is what the numbers actually look like:

ModelFP16 VRAMFP8/GGUF VRAMReduction
HunyuanVideo47-58 GB~8 GB (FP8+tiling)83-86%
Wan Video 2.1 14B54-65 GB~6 GB (GGUF 480p)89-91%
Wan Video 2.2 14B54-65 GB~6 GB (GGUF 480p)89-91%
LTX Video 2B14-22 GB6-8 GB (FP8+tiling)57-64%
LTX Video 13B28-45 GB14-18 GB (FP8)50-60%
CogVideoX 5B25-36 GB~16 GB (FP8)36-56%
CogVideoX 2B12-18 GB~8 GB (FP8)33-56%
Wan Video 2.1 1.3B9-13 GB4-6 GB (GGUF)54-56%
Mochi 1 Preview42-60 GB~20 GB (FP8)52-67%
AnimateDiff~6 GB~6 GBN/A

The key insight: FP8 and GGUF quantization hit video models harder than LLMs in terms of VRAM savings because video models carry massive VAE decoders and text encoders that compress extremely well. Tiling further reduces peak VRAM by processing spatial regions sequentially rather than all at once.

Why are the reductions so much larger than for LLMs? Video diffusion models have three major components that each consume VRAM independently: the text encoder (often T5-XXL at 9+ GB), the denoising backbone (DiT or U-Net), and the VAE decoder. Offloading the text encoder to CPU RAM eliminates one entire component from VRAM. Quantizing the backbone from FP16 to FP8 halves its footprint. Tiling the VAE decoder means only one spatial tile is in VRAM at a time. Stack all three techniques and the compounding savings are massive.

Use our diffusion calculator to check exact VRAM estimates for your specific GPU and configuration.

What You Can Run By GPU Tier

8 GB VRAM (RTX 4060, RTX 3060 8GB, RTX 4060 Laptop)

At 8 GB, video generation is real but constrained:

  • Wan Video 2.1 1.3B with GGUF: 4-6 GB at 480p. The best quality-per-VRAM ratio for budget GPUs. Short clips with decent motion.
  • LTX Video 2B with FP8+tiling: 6-8 GB. Fast generation, up to 720p with aggressive tiling.
  • AnimateDiff: ~6 GB. Animate any SD 1.5 checkpoint. Limited to 512x512, 16 frames.
  • CogVideoX 2B with FP8: ~8 GB. 480p, 49 frames at 8fps.
  • HunyuanVideo with FP8+tiling: ~8 GB. Yes, seriously. Community workflows have brought this 13B model down to 8 GB VRAM at reduced resolution.

Verdict: Surprisingly capable. Wan 1.3B GGUF and LTX Video 2B FP8 are the go-to choices. Expect 480p output and generation times of 2-10 minutes per clip depending on the model and step count. System RAM matters here too — plan for at least 16 GB RAM to handle text encoder offloading alongside your GPU workload.

12 GB VRAM (RTX 4060 Ti, RTX 3060 12GB, RTX 4070)

The sweet spot for casual video generation:

  • Everything from the 8 GB tier, but faster and at higher resolution.
  • Wan Video 2.1 14B with GGUF+t5_cpu offload: runs at 480p with about 8 GB VRAM. The quality jump from 1.3B to 14B is significant even at lower resolution.
  • Wan Video 2.2 14B with GGUF: same VRAM profile as 2.1 14B with improved output quality.
  • LTX Video 2B at FP8 without tiling: full 720p comfortably.
  • CogVideoX 5B starts to become viable with aggressive offloading.

Verdict: 12 GB opens the door to 14B-class models through quantization. This is where video generation becomes genuinely useful for creative work. The RTX 3060 12GB remains one of the best value propositions for entry-level video generation given its low used-market price and sufficient VRAM.

24 GB VRAM (RTX 4090, RTX 3090, RTX 5090)

The enthusiast tier. Every model runs at usable quality:

  • Wan Video 2.1 14B at FP8: 720p without aggressive offloading. Best open-source video quality.
  • Wan Video 2.2 14B at FP8: same story, better results.
  • LTX Video 13B at FP8: 14-18 GB. Higher quality than the 2B variant with excellent generation speed.
  • HunyuanVideo at FP8: fits comfortably. 720p output with strong temporal coherence.
  • CogVideoX 5B at FP8: ~16 GB. Runs with room to spare.
  • Mochi 1 Preview at FP8: ~20 GB. Finally accessible on consumer hardware.

Verdict: The RTX 4090 remains the king of local video generation. At 24 GB, you never need to choose between models — they all fit.

48 GB+ (RTX A6000, dual GPUs, Apple Silicon 64GB+)

For production workflows and maximum quality:

  • Every model at FP16 for reference-quality output.
  • Wan Video 14B at 720p, no offloading, full precision.
  • HunyuanVideo at FP16 with 129 frames at 24fps.
  • Mochi 1 Preview at FP16.
  • Batch generation and LoRA training become practical.
  • Apple Silicon Macs with 64-128 GB unified memory can load any model, though generation is slower than equivalent NVIDIA hardware.

Model-by-Model Breakdown

Wan Video Family — The Quality Leaders

The Wan Video 2.1 14B remains the quality benchmark for open video generation. Its successor, Wan Video 2.2 14B, improves prompt adherence and motion naturalness. Both support text-to-video and image-to-video.

The breakthrough for consumer hardware is GGUF quantization combined with T5 text encoder offloading to CPU. This moves 9.4 GB of text encoder weights to system RAM, and GGUF compresses the remaining DiT weights dramatically. The result: a 14B model running at 6 GB VRAM for 480p generation.

Wan Video 2.1 1.3B is the distilled lightweight variant. At 4-6 GB GGUF, it is the most accessible video model for budget hardware. Quality is noticeably below the 14B variants but still produces coherent short clips.

The Wan Video 2.2 TI2V 5B variant is a text-and-image-to-video model that bridges the gap between the 1.3B and 14B tiers in both quality and VRAM requirements.

LTX Video — Speed Champions

LTX Video 2B from Lightricks is the fastest video model in this list. The distilled variant generates clips in under a minute on modern GPUs. With FP8+tiling, it fits on 6-8 GB VRAM while supporting up to 720p and 161 frames.

LTX Video 13B trades some of that speed for substantially better quality. At FP8, it needs 14-18 GB, making the RTX 4090 its natural home. Available in dev, distilled, and FP8 variants.

HunyuanVideo — The FP8 Success Story

HunyuanVideo from Tencent is the poster child for why FP8 matters. Its FP16 requirements (47-58 GB) put it firmly in datacenter territory. But FP8 quantization combined with tiling workflows have brought it down to approximately 8 GB VRAM — a reduction that seemed impossible a year ago.

At 24fps with up to 129 frames, HunyuanVideo produces some of the smoothest output of any open model. The higher native framerate gives it a significant edge for clips where fluid motion matters — action sequences, camera pans, and character animation. If you have a 24GB GPU, this is one of the best options available.

CogVideoX — Research Workhorses

CogVideoX 5B and CogVideoX 2B from Tsinghua University are solid mid-range options. The 5B variant at FP8 (~16 GB) delivers good 480p output with 49 frames at 8fps, producing approximately 6-second clips. The 2B variant at FP8 (~8 GB) is one of the more accessible options for low-VRAM setups. Both use a 3D full-attention transformer architecture with expert adaptive LayerNorm.

CogVideoX is a good choice when you want a straightforward model without the complexity of offloading configurations. At FP8, the 5B variant fits neatly on a 24 GB GPU with plenty of room to spare, and the 2B variant works on 8-12 GB cards without any offloading tricks.

Mochi 1 Preview and AnimateDiff

Mochi 1 Preview is a 10B model with strong motion quality and an Apache 2.0 license. At FP8 (~20 GB), it fits on high-end consumer GPUs. Not the most efficient option but notable for its permissive licensing.

AnimateDiff remains relevant as a motion adapter for Stable Diffusion 1.5. At ~6 GB total, it is the lightest video option available — though limited to 512x512 and 16 frames. Its unique strength is compatibility with the entire SD 1.5 LoRA and checkpoint ecosystem: any style, character, or concept LoRA trained for SD 1.5 works with AnimateDiff out of the box, making it ideal for stylized animations and artistic projects where visual consistency with existing image workflows matters.

ComfyUI for Video Generation

ComfyUI is the dominant tool for local AI video generation, and most of the low-VRAM configurations in this guide originated from its community. Here is why it matters:

Offloading control. ComfyUI nodes let you specify exactly which components load on GPU versus CPU. The t5_cpu workflows for Wan Video, the tiling nodes for HunyuanVideo, and FP8 casting nodes all exist because ComfyUI's architecture makes this kind of fine-grained memory management possible.

Workflow sharing. The ComfyUI community shares complete JSON workflows that encode every setting — precision, offloading, tiling, scheduling. When someone figures out how to run HunyuanVideo on 8 GB, they export a workflow that anyone can load and reproduce exactly.

Model compatibility. Every model in this guide has ComfyUI support through either official nodes or community extensions. Video-specific nodes handle frame interpolation, upscaling, and export.

Getting started. Install ComfyUI, then add the relevant custom node pack for your chosen video model. Load a community workflow JSON file for your VRAM tier. Most workflows include precision and offloading settings already tuned for specific GPU memory targets. Adjust frame count and resolution from there.

If you are new to ComfyUI, our ComfyUI beginner's guide covers the fundamentals. The same concepts apply to video workflows with the addition of temporal dimensions.

Resolution, Frame Count, and VRAM

VRAM usage scales with both spatial resolution and temporal length. A few rules of thumb:

  • Doubling resolution (e.g., 480p to 720p) roughly doubles VRAM for the VAE and increases backbone VRAM by 30-50%.
  • Doubling frame count increases VRAM proportionally for the temporal attention layers. Going from 49 to 97 frames can add 2-4 GB depending on the model.
  • Tiling breaks this scaling by processing one spatial region at a time, making high resolutions possible on low VRAM at the cost of generation speed and occasional tile boundary artifacts.

For most consumer GPU users, 480p at 49-81 frames is the practical sweet spot. It generates quickly, fits in limited VRAM, and produces clips that look good on social media and in creative workflows. Scale up resolution only when your VRAM headroom allows it.

System RAM and Storage Requirements

GPU VRAM gets all the attention, but video generation also demands significant system RAM and fast storage:

System RAM. When offloading text encoders or model components to CPU, they live in system RAM. Wan Video 14B with t5_cpu offload moves ~9.4 GB to RAM. Running FP8 models with CPU offloading can require 24-32 GB of system RAM alongside your GPU memory. For 8 GB GPU setups using aggressive offloading, 32 GB system RAM is strongly recommended.

Storage. Video model weights are large. HunyuanVideo is approximately 25 GB on disk at FP16, ~13 GB at FP8. Wan Video 14B is similar. GGUF variants are smaller but still multi-gigabyte. A collection of video models easily exceeds 100 GB. Use an NVMe SSD — model loading from a hard drive adds minutes to every generation.

Generated output. Video files accumulate quickly. A single 5-second 720p clip at 24fps is relatively small as a compressed MP4 (1-5 MB), but raw frame sequences during generation can temporarily consume 500 MB to several GB. Keep at least 50 GB free on your generation drive.

Choosing Your Path

The right model depends on your VRAM budget and quality expectations:

The bottom line: FP16 VRAM numbers are not your actual hardware requirement. Check the FP8 and GGUF columns, use our diffusion calculator to verify against your specific GPU, and do not let headline numbers scare you away from local video generation.

Final Thoughts

Video generation on consumer hardware went from impossible to routine in under a year. The combination of FP8 quantization, GGUF weight formats, aggressive tiling, and community-driven offloading workflows has democratized what was recently a datacenter-only capability. An 8 GB GPU can now produce AI video. A 24 GB GPU can run every model available. The only question left is which model fits your creative goals.

Frequently Asked Questions

How much VRAM do I need for AI video generation?

It depends entirely on the model, precision, and offloading strategy. At FP16, models like HunyuanVideo need 47-58 GB, which is datacenter territory. But with FP8 quantization and tiling, HunyuanVideo drops to around 8 GB. Smaller models like Wan Video 1.3B run on just 4-6 GB with GGUF. The headline FP16 numbers are misleading for practical local use.

Can I generate AI video on an 8GB GPU like the RTX 4060?

Yes. Several models now fit on 8GB VRAM with quantization. Wan Video 2.1 1.3B runs at 4-6 GB with GGUF quantization. LTX Video 2B fits at 6-8 GB with FP8 and tiling enabled. AnimateDiff needs only about 6 GB total. You will be limited to 480p resolution and shorter clips, but usable video generation is absolutely possible on budget hardware.

Is FP8 quantization safe for video generation quality?

FP8 quantization preserves video quality remarkably well for most models. The visual difference between FP16 and FP8 output is minimal in blind tests, especially at 480p and 720p resolutions. The VRAM savings are dramatic, often cutting requirements by 50-70 percent. For local use, FP8 is now the recommended default for any model that supports it. GGUF quantization at Q4 or Q5 introduces slightly more artifacts but remains very usable.

What is the best GPU for AI video generation in 2026?

The RTX 4090 with 24 GB VRAM remains the sweet spot for serious local video generation. It runs every model in this guide at FP8 precision, including HunyuanVideo and Wan Video 14B at 720p. The RTX 5090 with 32 GB offers headroom for higher resolutions. For budget builds, the RTX 4060 Ti 16GB or RTX 3060 12GB handle smaller models well with quantization.

What is the difference between Wan Video 2.1 and Wan Video 2.2?

Wan Video 2.2 14B is the successor to 2.1 14B, offering improved temporal coherence, better prompt adherence, and refined motion quality. It also introduces a 5B text-and-image-to-video variant (TI2V) that is more accessible on consumer hardware. VRAM requirements are similar to the 2.1 generation. If you are starting fresh, Wan Video 2.2 is the better choice.

Can I use ComfyUI for AI video generation?

Yes, ComfyUI is the most popular tool for local AI video generation. It supports every model in this guide through community nodes and official integrations. ComfyUI's node-based workflow lets you chain text encoders, denoisers, and VAE decoders with fine-grained control over offloading and precision. Most of the low-VRAM configurations described here were pioneered by ComfyUI community workflows.