Video Generation
AI Video Generation on High-VRAM GPUs
Run AnimateDiff, CogVideo, Mochi, and emerging video AI models on H100 and A100 GPUs. Distributed processing, auto-scaling, and the compute power video generation demands.
TL;DR
- →Video models need VRAM — 24GB minimum, 48-80GB+ for frontier models
- →H100 & A100 available — 80GB HBM for the most demanding video workloads
- →Distributed processing — split video generation across multiple GPUs
- →RTX 4090 for lighter models — AnimateDiff and similar at $0.49/hr
The AI Video Generation Landscape in 2025
AI video generation is the next frontier of generative media. While image generation has matured into a production-ready technology, video generation is following the same trajectory — and moving fast. Open-source video models are now capable of generating coherent, multi-second clips that would have been impossible two years ago.
The challenge? Video generation is computationally expensive. A single 4-second clip at 512×512 can take 2-10 minutes on a high-end GPU. Higher resolutions, longer durations, and more sophisticated models push requirements into multi-GPU territory. This is where VectorLay's distributed infrastructure becomes essential.
Whether you're building the next Runway alternative, creating automated video content pipelines, or researching novel video architectures, VectorLay provides the GPU power you need — from single RTX 4090s for lightweight models to multi-H100 clusters for frontier research.
Video AI Models You Can Run on VectorLay
AnimateDiff
Turn any Stable Diffusion checkpoint into a video generator. AnimateDiff adds temporal motion modules to existing image models, producing smooth 16-24 frame animations. Supports ControlNet for guided motion. One of the most accessible entry points into AI video.
CogVideo / CogVideoX
Open-source text-to-video model from Tsinghua University. CogVideoX generates 6-second clips at 720×480 with impressive temporal coherence. The 5B parameter model requires significant VRAM — 40GB+ recommended for comfortable inference.
Mochi 1
Genmo's open-source video model, one of the first to approach commercial quality in the open-source space. Generates smooth, temporally coherent video with good prompt adherence. High VRAM requirements due to its asymmetric diffusion architecture.
Stable Video Diffusion (SVD)
Stability AI's image-to-video model. Takes a single image and generates a short video sequence from it — ideal for product animations, motion graphics, and creative content. Lighter than text-to-video models.
Open-Sora & Emerging Models
The open-source community is rapidly building alternatives to closed-source models like Sora (OpenAI), Runway Gen-3, and Kling. Open-Sora, LTX Video, HunyuanVideo, and others are pushing the boundaries of what's possible with open weights. VectorLay runs them all.
Why Video Generation Needs High-VRAM GPUs
Video generation is fundamentally more demanding than image generation. Here's why:
This is why VectorLay offers H100 and A100 GPUs alongside consumer hardware. For video generation, you often genuinely need the 80GB of HBM that enterprise GPUs provide.
GPU Recommendations for Video Generation
| Model Type | Recommended GPU | VRAM | Use Case |
|---|---|---|---|
| AnimateDiff, SVD | RTX 4090 | 24GB | SD-based video, short clips, img2vid |
| CogVideoX, Mochi | A100 (80GB) | 80GB | Text-to-video, longer clips |
| Open-Sora, LTX Video | H100 (80GB) | 80GB | High-quality text-to-video, longer durations |
| Frontier / Research | Multi-H100 cluster | 160-640GB+ | HD video, 10+ sec, novel architectures |
Distributed Video Processing on VectorLay
Video generation often exceeds what a single GPU can handle efficiently. VectorLay supports multi-GPU deployments that let you distribute video workloads:
Tensor Parallelism
Split a single model across multiple GPUs when it doesn't fit in one GPU's VRAM. Run 10B+ parameter video models across 2-8 H100s with near-linear scaling.
Temporal Tiling
Generate long videos by splitting them into overlapping temporal segments, each processed on a separate GPU. Segments are blended for seamless transitions — turning a 4-second limit into 30+ second outputs.
Batch Parallelism
Process multiple video generation requests simultaneously across a fleet of GPUs. Perfect for platforms serving many users or batch processing content pipelines. Auto-scaling adds GPUs as queue depth grows.
Pipeline Parallelism
Chain video generation with post-processing — upscaling, interpolation (RIFE), audio sync, and encoding. Each stage runs on the optimal GPU while the pipeline streams data between stages.
Building a Video AI Platform on VectorLay
Whether you're building a Runway competitor, a social media content tool, or an internal video automation pipeline, VectorLay provides the infrastructure:
Text-to-Video Platforms
Build user-facing video generation products. VectorLay handles auto-scaling to match demand, fault tolerance for reliability, and per-minute billing to control costs during low-traffic periods.
Automated Content Pipelines
Generate social media videos, ad creatives, and marketing content at scale. Feed scripts in, get finished videos out. Combine video generation with voice AI for complete automated production.
Research & Development
Experiment with novel video architectures, train custom video models, or fine-tune existing ones. Access H100 clusters for training runs without long-term commitments.
Post-Production Tools
AI-powered video editing, style transfer, object removal, super-resolution, and frame interpolation. Run multiple post-processing models on dedicated GPUs for real-time editing workflows.
VectorLay vs. Alternatives for Video AI
The video generation space is evolving rapidly. Here's how different approaches compare:
| Approach | Pros | Cons | Best For |
|---|---|---|---|
| VectorLay | Affordable H100/A100, fault tolerance, scale on demand | Self-managed models | Platforms, pipelines, research |
| Runway API | Best quality, simple API | $0.05+/sec of video, closed model, rate limits | Low-volume, quality-first |
| Replicate | Easy API, many models | Expensive per-second billing, cold starts | Prototyping, low volume |
| AWS (A100/H100) | Enterprise SLAs, compliance | $3-5+/hr per GPU, complex setup | Enterprise with existing AWS |
| Local Hardware | No recurring cost | $20K+ upfront per H100, maintenance, power | 24/7 research with large budget |
The Future of Video AI Is Open Source
Just as Stable Diffusion democratized image generation, open-source video models are following the same trajectory. Every few months, a new open-weight model closes the gap with closed-source leaders. The models that are state-of-the-art today will be commodity open-source in 12-18 months.
Building on open-source means you're not locked into a single provider's API, pricing, or content policies. You can fine-tune models on your own data, customize generation parameters, chain models together, and build differentiated products instead of competing on the same API everyone else uses.
VectorLay is the infrastructure layer for this open-source video revolution. We provide the GPUs, the fault tolerance, and the scaling — you bring the models and the vision.
Getting Started with Video Generation on VectorLay
Assess Your VRAM Needs
AnimateDiff and SVD fit on an RTX 4090 (24GB). CogVideo, Mochi, and most text-to-video models need A100/H100 (80GB). Multi-GPU for frontier work.
Deploy Your Container
Package your video generation pipeline as a Docker container. Use community images or build custom ones. VectorLay handles GPU passthrough and networking.
Scale as Needed
Start with a single GPU for development. Scale to multi-GPU clusters for production. Auto-scaling adds capacity during peak demand and scales down overnight.
Power your video AI pipeline
From AnimateDiff on a single RTX 4090 to frontier video models on H100 clusters — VectorLay provides the GPU compute video generation demands, with built-in fault tolerance and auto-scaling.