The maturity of AI in your organization massively impacts the type of AI solution that will work best for you and what sort of hardware vendor would make the best infrastructure partner. Below are tips based on three common situations:
Depending on the size of your team and the functions needed from your AI, you should be looking at either a workstation or a small, project-scale cluster.
A workstation is preferable if you only have one or two people conducting the analysis. It is important to buy your AI workstation from an organization that specializes in GPU-accelerated computing and not just general hardware.
This ensures that the organization you purchase the AI workstation from will have the right relationships with the right component suppliers. It should also have access to engineers who can ensure strong ROI for AI workloads based on the understanding that AI puts a significant burden on hardware.
If your team is a bit larger than one or two people conducting the AI analysis, a small AI cluster may be a good starting point. Clusters can be custom engineered to meet your specific project needs.
If you are looking to upgrade existing equipment, remember that your unique needs are much more likely to be met with a vendor with expertise in HPC and AI technology. Focused vendors will have close, deep relationships with leading-edge partners, so they can get the latest technology as soon as it's available. Similarly, well-established HPC and AI vendors often have greater purchasing power with these vendors, and therefore more competitive cost-to-performance than smaller or less focused hardware vendors.
Also, consider whether you might need some professional services help (i.e., cluster training or just rack-and-stack) to help you make the most of your cluster. If so, ensure your chosen cluster partner can provide that.
When you’re looking to grow, it’s important to not get caught up in hype. Focus on technologies you really need and allow your infrastructure design team to use more traditional technology in other areas. This allows you to spread your budget out further for more nodes (which are key to training the system).
When planning to take your AI programs to scale, consider future-proofing that investment with a scalable solution. You may want to scale even further in the near to midterm future, so a vendor that designs systems with a building block approach (which intentionally prepares the cluster for future growth) is key. This way, you are not locked into a total system refresh in the future, and can grow your environment without breaking the bank.
Your goal should always be to build an AI solution tailored to not only your technical needs but also your organizational needs. With modular design, you can scale as you need based on staff readiness, budget, and other criteria.
Contact us to speak to a specialist about meeting your AI needs.