Table of Contents
Video Generation VRAM Overview
AI video generation is one of the most VRAM-intensive workloads in the AI ecosystem. Unlike image generation, video models must process temporal dimensions alongside spatial ones, causing VRAM to scale with both resolution and frame count. Running video generation on a dedicated GPU server requires careful planning. For general AI video generation hosting, 24 GB VRAM is the practical minimum for production-quality output.
VRAM Requirements by Model
| Model | FP16 VRAM (base) | FP16 VRAM (generation) | INT8 VRAM |
|---|---|---|---|
| AnimateDiff (SD 1.5) | ~4 GB | ~8-12 GB | ~5-8 GB |
| Stable Video Diffusion (SVD) | ~8 GB | ~14-20 GB | ~10-14 GB |
| Wan AI (1.3B) | ~3 GB | ~8-12 GB | ~5-8 GB |
| Wan AI (14B) | ~28 GB | ~35-50 GB | ~20-30 GB |
| CogVideoX-5B | ~10 GB | ~18-24 GB | ~12-16 GB |
Generation VRAM is substantially higher than model weight VRAM because the model must hold intermediate frames, temporal attention caches, and VAE buffers simultaneously. Peak usage occurs during the diffusion sampling loop.
Resolution and Frame Count Impact
| Model | Resolution | Frames | FP16 VRAM |
|---|---|---|---|
| AnimateDiff | 512×512 | 16 frames | ~8 GB |
| AnimateDiff | 512×512 | 32 frames | ~14 GB |
| SVD | 576×1024 | 14 frames | ~16 GB |
| SVD | 576×1024 | 25 frames | ~22 GB |
| Wan AI 1.3B | 480×832 | 81 frames | ~10 GB |
| CogVideoX-5B | 720×480 | 48 frames | ~22 GB |
VRAM scales roughly linearly with frame count. Doubling the number of frames approximately doubles the temporal attention memory. Higher resolution increases VRAM quadratically due to the spatial dimensions. For image generation VRAM comparisons, see our Stable Diffusion VRAM guide.
GPU Recommendations
| GPU | VRAM | Video Generation Capability |
|---|---|---|
| RTX 4060 | 8 GB | AnimateDiff short clips (16 frames, 512×512) |
| RTX 4060 Ti | 16 GB | AnimateDiff (32 frames), SVD short, Wan 1.3B |
| RTX 3090 | 24 GB | SVD full, CogVideoX-5B, Wan 1.3B comfortable |
| Multi-GPU (2x RTX 3090) | 48 GB | Wan 14B, long-form generation |
The RTX 3090 with 24 GB is the sweet spot for most video generation models at standard resolution and frame counts. The RTX 4060 Ti handles lighter models like AnimateDiff and Wan 1.3B.
Model Comparison
AnimateDiff extends Stable Diffusion 1.5 with temporal layers, making it the lightest option but limited to SD 1.5 quality. SVD produces higher-quality video from image prompts. Wan AI offers both lightweight (1.3B) and high-quality (14B) options with text-to-video capability. CogVideoX provides the best text-to-video quality at the 5B scale but requires 24 GB minimum.
For image generation comparisons, see our Flux.1 VRAM requirements. Compare GPU options with the GPU comparisons tool.
Deployment Recommendations
Start with AnimateDiff or Wan 1.3B on a 16 GB GPU to evaluate video generation for your use case before investing in larger GPUs. For production quality, the RTX 3090 running SVD or CogVideoX provides the best single-GPU results. Use ComfyUI for workflow-based video generation with AnimateDiff nodes.
Estimate costs with the cost calculator. Read the self-host guide for server setup. Browse all generation guides in the model guides section.
Host AI Video Generation on Dedicated GPUs
Generate AI videos on dedicated GPU servers with 16-48 GB VRAM. No generation limits and full root access.
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