Generative AI is changing artificial intelligence from a technology used only by experts to one that defines the enterprise—used by every employee and engaging every customer. McKinsey estimates that generative AI could add between $200 billion and $340 billion in value to banking alone. Add scientific research, healthcare, retail, media and…well…virtually every industry in the world. And AI-driven video is the next wave. From gaming and advertising to surgical and scientific modeling and simulations, the use cases—and potential value—are nearly limitless.
This is exciting, but a real problem for companies looking to be a part of the video AI evolution. There is a very real shortage of GPUs, especially those powerful enough for video data models.
Why generative AI for videos is so resource intensive.
Generative AI for videos is a complex and resource-intensive process that requires a significant amount of GPU compute power. This is because generating realistic and high-quality videos involves analyzing and processing a large amount of visual data in real-time. And unlike other forms of AI data modeling, such as natural language processing or image recognition, generative AI for videos needs to consider not only the individual frames but also the transitions between them. 4K and 8K workloads at 60fps while doing object detection and motion tracking means the requirements for compute and memory bandwidth are not getting smaller; add in live-streaming events and the need is only growing. This adds an extra layer of complexity to the computational requirements, as it requires the algorithm to continuously learn and adapt while generating each frame. As a result, generative AI for videos takes more GPU compute than other AI data modeling methods, making it one of the most demanding applications in terms of hardware resources.
How to get the GPUs you need for AI innovation.
Assuming you have the money and can predict your GPU needs for 6 months from now, then you can place an order to purchase powerful NVIDIA H100s or even the new GH200s. There is significant risk in this approach. Will you have enough compute power for developing your models? Will a big player buy up the supply before you? Will a B100 be released that gives competitors an edge?
The less risky approach is to rent GPUs when you need them. Instead of waiting for months on an order, you can get same day access to begin running analyses and testing models.
Valdi has access to tens of thousands of GPUs, allowing instant access to the latest silicon from Nvidia. Through the Valdi platform, you can quickly provision on-demand HPC resources or select to reserve capacity for long-term usage.
Not only does Valdi have supply, but the team has started evaluating applied MLOps and model deployments. Exploration is ongoing to forklift workloads from one GPU platform to another and optimizing inference. Recent experimentation has been with GH200s and getting familiar tools (Pytorch, etc.) working as-expected on the new architecture. Comparing inference between the H100s and Grace-Hopper means the tooling needs to work smoothly on ARM-based compute. In addition, Valdi is integrating more tightly with Storj distributed data storage to allow easy movement of the entire training and inference workflow, allowing optimized work among available GPU resources and tooling.
The benefits of using on-demand GPUs.
On-demand GPUs offer key benefits for AI work. They let users access powerful computing when needed, without buying expensive hardware. This saves money and allows for easy scaling. Users can tap into the latest GPU technology without constant upgrades. Many services include ready-to-use software, saving setup time. This makes advanced AI work possible for smaller teams and individuals. It also speeds up development and testing of new ideas.
- No need to make a huge capital investment.
- Try before you buy.
- No waiting, no contracts, no sales engagements.
- Easy to scale.
- Get access to the latest technology.
- Faster to get started with setup software.
Overall, on-demand GPUs make high-performance computing more accessible and flexible for AI projects. And utilizing existing resources more efficiently is better for the environment. AI researchers and developers can be well equipped to test ideas and innovate quickly both for the current era of AI and the quickly approaching video era. The availability, ease of use, and flexibility of on-demand GPUs makes that possible for everyone looking to make advancements in AI.
You can start using GPUs today at valdi.ai
