In an enterprise AI training cluster, GPUs often sit idle, waiting for training data to be read from storage and loaded into memory—a state known as I/O starvation. To maximize GPU utilization and data throughput, which storage hardware strategy should you implement?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal: if your GPUs are sitting idle waiting on storage, you're burning money. Use PCIe-based NVMe SSDs because they connect directly to the PCIe bus, slashing latency and keeping data flowing to the GPUs.
Full Explanation
In high-performance computing (HPC) and AI training environments, data ingestion is a critical phase. Deep learning models require massive datasets containing millions of files (images, text, or audio) to be continuously loaded into GPU memory during training epochs. If the storage subsystem cannot deliver this data quickly enough, the GPUs experience 'I/O starvation' and stand idle, reducing overall efficiency.
To resolve this bottleneck, the best strategy is to deploy PCIe-based Non-Volatile Memory Express (NVMe) SSDs. Unlike legacy storage interfaces such as SATA or SAS, which were designed for mechanical hard drives and rely on protocols with high latency and low queue depths, NVMe was built from the ground up for solid-state storage. NVMe SSDs interface directly with the high-speed PCIe bus. This direct connection dramatically reduces latency and enables massive parallel queue depths (up to 64,000 queues with 64,000 commands each), providing the high read throughput and random I/O performance required to feed modern GPUs.
Let's evaluate the incorrect options. Layering a software-defined distributed file system over standard mechanical hard drives will not solve the underlying hardware bottleneck; spinning HDDs have physical seek times that are far too slow for AI workloads. Standard high-capacity SATA SSDs configured in a RAID 5 array for combined redundancy, while faster than HDDs, are capped by the legacy SATA III interface (around 600 MB/s), which is insufficient for high-density GPU clusters. While RAID configurations can improve throughput, standard SATA-based RAID 5 is still bottlenecked by the controller interface and write performance overhead. Implementing network-attached storage (NAS) using standard Gigabit Ethernet links introduces severe network bottlenecks, as a 1 Gbps link can only deliver roughly 125 MB/s of throughput, which is vastly inadequate for modern AI training demands.