How Cloud GPUs Help Create Realistic Content for AI Video Generation

AI-driven video generation has come a long way from basic frame interpolation and facial recognition. Today, models powered by GANs, diffusion techniques and transformers can synthesise hyper-realistic footage, animate still images and generate full-length videos from simple text prompts. Tools like RunwayML and Synthesia are making this more accessible than ever.

But the real challenge? It is the compute. 

AI video generation is one of the most computationally demanding tasks. Processing high-resolution frames at scale requires massive parallelism, far beyond what CPUs can handle. That’s why everyone is opting for cloud GPUs, getting the compute power needed to train and deploy these models efficiently. Whether you're experimenting with AI video generation models or creating your own, the right infrastructure makes all the difference.

Types of AI Video Generation

AI video generation can be categorised into three types: Text-to-Video (T2V), Image-to-Video (I2V) and Video-to-Video (V2V). Each method utilises deep learning models and heavy computational resources to create dynamic and realistic visuals. 

Here’s how each type works:

1. Text-to-Video (T2V): T2V models generate videos from textual descriptions, using natural language processing (NLP), generative adversarial networks (GANs) or diffusion models. They create realistic animations, cinematic sequences or explainer videos without requiring pre-existing visual input. 
2. Image-to-Video (I2V): I2V models transform static images into animated sequences by predicting motion, depth and transitions. They use deep learning to extrapolate movement and bring still images to life. 
3. Video-to-Video (V2V): V2V models enhance or modify existing videos using AI. They can upscale resolution, change styles, add effects or even generate new frames for smoother motion. 

Understanding the Challenge for AI Video Generation 

AI video generation is a resource-hungry task. Text-to-Video (T2V), Image-to-Video (I2V) and Video-to-Video (V2V) generation all demand heavy computation. In T2V, the AI must synthesise entire frame sequences from a prompt, often using diffusion or transformer models with tens of billions of parameters. While I2V and V2V tasks add constraints like preserving a source image’s style or a video’s structure, they still require generating many high-resolution frames with temporal consistency. 

For instance, generating hundreds of coherent frames at decent frame rates and resolutions is highly compute-intensive. It involves handling billions of parameters and performing trillions of mathematical operations. Such tasks may include:

1. Training Phase: Feeding massive datasets of video and image content into models to teach them patterns, textures, and movements.
2. Inference Phase: Using the trained model to generate new video content, often in near-real time (seconds to minutes) or approaching real-time with advanced hardware.
3. Post-Processing: Refining outputs with effects like upscaling, colour correction or audio synchronisation. 

How Cloud GPUs Help in AI Video Generation

Traditional CPUs are designed for general-purpose computing so they struggle to meet these demands. While GPUs excel at parallel processing by performing thousands of calculations, their high-bandwidth memory accelerates neural network inference/training, which otherwise would take hours on a CPU to be completed in minutes or seconds. However, not all GPUs are designed to support such tasks and local hardware can be prohibitively expensive or insufficient for large-scale video generation projects. This is where you can opt for cloud GPUs to accelerate your workloads. 

Unmatched Processing Power

Cloud GPUaas platforms like Hyperstack offer instant access to high-end GPUs like the NVIDIA A100 and NVIDIA H100 with tensor cores optimised for AI. These GPUs can process massive neural networks swiftly, cutting training times for video generation models from weeks to days or hours. For example, an open-source model can produce a 15-second 720p clip (360 frames at 24 fps) with cinematic detail but only when running on a GPU with 80 GB memory like an NVIDIA A100 or NVIDIA H100 to handle the load. 

Similar Read: Best Open Source Video Generation Models in 2025

Flexibility and Scalability

Cloud GPUaas platforms make powerful hardware like the NVIDIA H100 SXM and NVIDIA H100 PCIe within everyone’s reach. With Hyperstack’s 1-click deployment, you can start using our cloud GPUs in just minutes. Whether you’re creating short AI-generated video clips for a small business or producing hours of content for a studio, you can easily scale your projects without budget concerns. Renting our cloud GPUs for AI lets you pay only for the resources you use, avoiding the expense of idle hardware.

Cost Efficiency 

Unlike local GPUs, which incur maintenance, power and other costs, cloud GPUs shift these to providers. This offers long-term savings for projects with fluctuating needs, as you avoid upfront investments and just pay for what you use. For example, you can access our cloud GPUs for AI like the NVIDIA H100 PCIe for $1.90 per/hr.

However, not every workflow requires the absolute top-end. You can also opt for the NVIDIA RTX A6000 or the NVIDIA L40 with a sufficient 48GB memory and strong compute at a lower cost. It’s a cost-effective choice for developers experimenting with AI video or running less demanding models. Many open-source models can be fine-tuned or run with lower memory GPUs, so a single NVIDIA L40 (48 GB) can create high-quality videos. For example, the latest Wan 2.1 model can be run on as little as 8.19GB VRAM while scaling up to 48GB for higher-quality outputs. In our tutorial on running the Wan 2.1 model, we used the NVIDIA L40 for a small model (1.3B) and the NVIDIA A100 for a large model (14B).

Explore our comprehensive tutorial on Wan 2.1 here to discover how we created the lifelike cat below.

Get Started with AI Video Generation Models using Hyperstack Cloud GPUs, starting at $0.50 per/hr.

FAQs

Why do AI video generation models need GPUs?

AI video models perform trillions of calculations for rendering frames, which require massive parallelism- something GPUs handle much better than CPUs.

What types of AI video generation exist?

There are three main types: Text-to-Video (T2V), Image-to-Video (I2V), and Video-to-Video (V2V), each using deep learning for realistic animation and enhancement.

Why choose cloud GPUs over local GPUs?

Cloud GPUs provide on-demand access to high-end hardware, eliminating upfront costs and allowing users to scale their workloads as needed.

Which GPUs are best for AI video generation?

The NVIDIA A100 and NVIDIA H100 are ideal for high-end models, while the NVIDIA RTX A6000 and NVIDIA L40 offer cost-effective options for smaller projects.

How do cloud GPUs reduce costs for AI video generation?

Instead of investing in expensive hardware, you can rent GPUs by the hour, paying only for the compute resources you use. Check out our cloud GPU pricing here.