AI video generation is one of the most GPU-hungry workloads in the AI space right now. Models like Stable Video Diffusion, Runway Gen-3, Sora-class architectures, and CogVideoX need serious VRAM and compute throughput. Here's what you actually need — and what you can get away with.
The short version: For generating short clips (4-16 seconds) at 720p, an RTX 4090 with 24GB VRAM handles it well at $0.18/hr on io.net. For production pipelines generating longer, higher-resolution video, you'll want A100 80GB ($1.49/hr) or H100 ($2.20/hr) GPUs with their larger memory pools and faster memory bandwidth.
GPU Requirements by Video Model
Different models have wildly different hardware appetites:
Stable Video Diffusion (SVD/SVD-XT):
- Minimum: 16GB VRAM (RTX 4090 comfortably, A100 40GB)
- Generates 14-25 frames at 576x1024
- Generation time: 45-90 seconds per clip on RTX 4090
- Monthly cost for 1,000 clips/day: ~$32 on io.net (RTX 4090)
CogVideoX / Open-source Sora alternatives:
- Minimum: 24GB VRAM for short clips, 48GB+ for longer sequences
- Generates up to 6 seconds at 720p
- Generation time: 2-5 minutes per clip on A100 80GB
- Monthly cost for 500 clips/day: ~$540 on io.net (A100 80GB)
Mochi 1 / LTX-Video:
- Works on 24GB GPUs with optimization
- 5-10 second clips at reasonable resolution
- Generation time: 60-120 seconds on RTX 4090
Training your own video model:
- Minimum: 8x A100 80GB or 8x H100
- Fine-tuning SVD on a custom dataset: 24-72 hours on 8x A100
- Cost: $860-$2,580 on io.net
Why Video Gen Needs More GPU Than Image Gen
A single Stable Diffusion image needs about 6-8GB of VRAM. Video generation needs 3-6x more, and the reasons are worth understanding if you're budgeting:
- Temporal attention layers — Video models process relationships between frames, not just individual images. This adds massive intermediate tensors that live in GPU memory during generation.
- Higher-dimensional latent space — Instead of a 2D latent (height x width), video uses a 3D latent (height x width x time). A 25-frame video at 576x1024 produces latents that are 25x larger than a single image.
- Memory bandwidth becomes the bottleneck — Video diffusion steps shuffle enormous tensors back and forth. The H100's 3.35 TB/s HBM3 bandwidth generates frames 2-3x faster than the 4090's 1.01 TB/s GDDR6X, even though raw FLOPS are only 1.5x different.
Production Video Generation Architecture
If you're building a product around AI video (marketing content, game assets, social media tools), here's a proven architecture on io.net:
Low volume (<100 videos/day):
- 1-2x RTX 4090
- Process sequentially or with simple queue
- Cost: $2.60-$8.64/day
- Latency: 1-3 min per video
Medium volume (100-1,000 videos/day):
- 2-4x A100 80GB behind a queue (Redis/RabbitMQ)
- Parallel generation with load balancing
- Cost: $71-$143/day
- Latency: 2-5 min per video, 10+ concurrent
High volume (1,000+ videos/day):
- 4-8x H100 SXM with auto-scaling
- Kubernetes-managed GPU pods
- Persistent model caching (avoid reload overhead)
- Cost: $211-$422/day
- Latency: 30-90 sec per video with batching
Optimization Tips for Video Workloads
There are meaningful savings available if you're willing to optimize:
Use half-precision (FP16/BF16) everywhere. Video models tolerate reduced precision well. This cuts VRAM usage in half and improves throughput by 30-50%.
Enable attention slicing. For models that support it (most diffusers-based models do), attention slicing processes the attention computation in chunks instead of all at once. Trades 10-15% speed for 30-40% memory reduction.
Cache the model in GPU memory between requests. Model loading takes 15-30 seconds. Keep it resident and process requests against the loaded model. This is the single biggest latency improvement for production APIs.
Render at lower resolution, then upscale. Generate at 512x320, then use a fast super-resolution model (Real-ESRGAN, 2 seconds on 4090) to hit 1080p. This is 4-6x faster than generating natively at 1080p.
Generate AI video on io.net — RTX 4090 from $0.18/hr, A100 from $1.49/hr. Launch GPU
