Originally published at: https://developer.nvidia.com/blog/tips-on-scaling-storage-for-ai-training-and-inferencing/
There are many benefits of GPUs in scaling AI, ranging from faster model training to GPU-accelerated fraud detection. While planning AI models and deployed apps, scalability challenges—especially performance and storage—must be accounted for. Regardless of the use case, AI solutions have four elements in common: Training model Inferencing app Data storage Accelerated compute Of these elements, data storage…
While writing this blog, I spoke with AI solutions creators and IT professionals. As a result, I learned of several important factors that are not always considered in deployments. This includes storage scalability, availability, and adaptability as these are not always fully evaluated. Additionally, I learned that even a well-designed POC does not necessarily address future adaptability and challenges. For those fully aware of the points made in this blog, I’m hopeful this will serve as a good checklist for future reference. For others, it’s my hope this will cause existing plans to be re-evaluated in the light of storage scalability for training and inference. I’d love to hear your comments and/or questions!