Building Power Efficient AI Data Centers with Solidigm QLC SSDs
-
Mitch Lewis
A Signal65 Lab Insight
AI has become a top priority for organizations due to its vast potential for innovation. Building AI data centers, however, presents significant challenges. AI is both computationally and data intensive, involving large infrastructure requirements and consequently, large power requirements. The massive amount of power required to support new AI data centers is a key challenge, adding cost, sustainability concerns, and limiting the total infrastructure that can be deployed in a single data center.
While energy-related concerns around AI are often focused on the widespread use of GPUs, the data storage required to store large scale AI training datasets and model checkpoints additionally have a significant impact on data center power efficiency. This study focuses on the role of network-attached data storage in AI datacenters, and evaluates how different storage media can impact power efficiency.
The goal of this study was to model a new, 100 Megawatt AI datacenter and evaluate the impact that different storage devices had on the total power efficiency. The study specifically evaluated the impact of QLC SSDs, TLC SSDs, and a hybrid HDD based deployment.
Research commissioned by:
