How Much VRAM for AI Video Generation in 2026? (Guide)

Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

AI video generation has a VRAM problem. Still-image models like SDXL or Flux can be squeezed into 12-16 GB with quantization tricks. Video models cannot — they must hold multiple frames in VRAM simultaneously, and the memory requirements scale aggressively with resolution and clip length. Here is exactly what each major tool needs.

Quick answer: Stable Video Diffusion works at 8 GB minimum. HunyuanVideo needs 24 GB or more. If you want to run serious local AI video generation, plan for 24 GB minimum.

See the recommended pick on the original guide

Why AI video needs so much VRAM

Static image diffusion generates one frame. Video models generate 16-121 frames at once. Each frame is a full image tensor, and the temporal attention layers need to attend across all frames simultaneously. A 5-second clip at 24fps means 120 frames in memory at once — approximately 8-15x the VRAM of a single image at the same resolution.

Additionally, video models use larger base architectures than image models. HunyuanVideo's transformer checkpoint alone is 40+ GB unquantized. Even aggressively quantized, the working VRAM requirement rarely drops below 18-20 GB for full operation.

VRAM requirements by tool

Stable Video Diffusion — the 8 GB baseline

Stable Video Diffusion generates short clips (14-25 frames) from a single input image. The original SVD model runs at 8 GB with careful settings — you need --medvram mode and frame generation at 576x1024 or lower. The SVD-XT extension pushes to 25 frames and needs 10-12 GB to avoid constant swapping.

SVD is dated by 2026 standards. The output is limited to 3-4 second clips, resolution is capped, and there is no text prompt input. It remains useful as an animation tool (bring a still image to life) but does not produce the kind of AI video that newer models do.

VRAM chart available at the original article

CogVideoX — the 16 GB sweet spot

CogVideoX-5B is a practical open-source video model that runs in 16 GB with INT8 quantization. It generates 6-second clips from text prompts at 720p, with quality that is genuinely useful. The 2B variant runs in 12 GB with better headroom.

For users with a 16 GB card (RTX 4060 Ti 16GB, RTX 5060 Ti, RTX 5070 Ti, RTX 5080), CogVideoX-5B with quantization is the best locally runnable option today. Expect generation times of 10-20 minutes per clip on a 16 GB card — this is slow, but it runs without cloud costs.

Wan2.1 — emerging open-source option

Wan2.1 is a strong contender from 2025-2026. The 14B model produces high-quality video output and runs on 16-24 GB with quantization. At 16 GB (with aggressive quantization), clips are short and generation is slow. At 24 GB, it runs more comfortably.

This is the model most recommended for users with an RTX 4090 who want the best locally-runnable video quality without paying for HunyuanVideo's full requirements.

HunyuanVideo — 24 GB minimum

HunyuanVideo is the state-of-the-art open-source video generation model as of 2026. It produces cinematic-quality 720p video at 3-10 second lengths. The requirements are brutal:

• Full fp16: ~40+ GB. RTX 4090 cannot run it.
• INT8 quantized: ~24 GB. RTX 4090 can run it, slowly.
• INT4 quantized: ~18-20 GB. Fits in RTX 4060 Ti 16GB with aggressive tuning, but quality degrades.

For HunyuanVideo at acceptable quality, the RTX 4090 (24 GB) is the minimum practical card. Expect 20-60 minutes per clip depending on length and settings. The RTX 5090 (32 GB) runs INT8 HunyuanVideo with more headroom and better speed.

GPU recommendations by budget

Which GPU should YOU buy?

You want to run SVD or CogVideoX-2B: 12-16 GB is enough. The RTX 4060 Ti 16GB at $400 or RTX 5060 Ti at $450 both work.

You want CogVideoX-5B or Wan2.1 at good quality: 16 GB with quantization works, but 24 GB is comfortable. The RTX 4090 hits the sweet spot here.

HunyuanVideo is your target: Do not buy anything with less than 24 GB. The RTX 4090 is the entry point. A used RTX 3090 (24 GB) at lower cost is viable but slower.

You want the absolute best local AI video: RTX 5090 (32 GB). Nothing else comes close for HunyuanVideo at quality settings with reasonable generation times.

Common mistakes to avoid

1. Buying 16 GB specifically for HunyuanVideo. It technically runs with INT4 quantization, but the quality loss is significant and generation is extremely slow. You will be disappointed.
2. Ignoring generation time. AI video is slow even on good hardware. A 5-second clip on a 24 GB card can take 20-40 minutes. Budget your expectations accordingly.
3. Treating AI video like AI images. The same tricks that reduce image model VRAM (tiled decoding, attention slicing) often do not work well for video models, which need the temporal context of the full sequence in memory.

Final verdict

AI video generation is the most demanding local AI workload in 2026. If your goal is running HunyuanVideo locally, 24 GB is the minimum — full stop. For lighter tools like SVD or CogVideoX, 16 GB works with quantization. See our Best GPU for AI Video for full recommendations, Best GPU for HunyuanVideo for that specific model, and How Much VRAM for AI for a broader breakdown across all AI workloads.

See the recommended pick on the original guide

See the recommended pick on the original guide

Related guides on Best GPU for AI

• Best GPU for HunyuanVideo (AI Video Generation) in 2026
• Best Quantization for Stable Diffusion & Flux
• Can the RTX 3060 Run Stable Diffusion? (Tested)

The full version lives on Best GPU for AI — VRAM calculator, GPU comparison table, and live Amazon pricing.