Scalable, high-performance AI depends on a storage infrastructure designed to handle massive volumes of data, rapidly and reliably. This article from Forbes examines how digital storage can either support or undermine AI performance, with real-world insights into latency, bandwidth, and capacity bottlenecks. Read the article to learn what to prioritize in your storage architecture, and reach out to Lotus Beta Analytics for help designing infrastructure that's AI-ready from the ground up.
How does digital storage technology relate to AI?
Digital storage technology is crucial for AI as it supports the storage and retrieval of large datasets required for training and inference. By combining SSDs and HDDs, data centers can optimize both cost and performance, ensuring that AI workloads run efficiently.
What are the benefits of using SSDs and HDDs together?
Data centers utilize SSDs for fast access to frequently used data, while HDDs are employed for storing less frequently accessed data at a lower cost. This hybrid approach allows for a balance between performance and cost-effectiveness, ultimately enhancing the overall efficiency of AI workloads.
What advancements have been made in digital storage for AI?
Recent advancements include Vdura's introduction of hybrid storage solutions that integrate QLC NAND flash SSDs with high-capacity HDDs. This combination aims to reduce flash capacity requirements by over 50% and lower power consumption, resulting in a total cost of ownership reduction of up to 60% for AI applications.
Sign in with LinkedIn.jpg)