A high-performance compute cluster is only as effective as its ability to access data. Your state-of-the-art GPUs will sit idle if they are constantly waiting for the storage system to feed them the next batch of data. This situation, known as an I/O bottleneck, can neutralize the performance benefits of an otherwise powerful server, extending training times and wasting expensive resources. Configuring a storage system that matches the throughput of your compute components is therefore a foundational part of designing an effective on-premise AI infrastructure.
For years, Serial ATA (SATA) solid-state drives (SSDs) were a significant upgrade over traditional spinning hard disk drives (HDDs). However, for modern AI workloads, even SATA SSDs can become a chokepoint. The SATA interface was originally designed for mechanical drives and is limited to a theoretical maximum bandwidth of about 600 MB/s.
This is where NVMe (Non-Volatile Memory Express) becomes the standard for high-performance systems. Unlike SATA drives, NVMe SSDs connect directly to the motherboard's PCIe (Peripheral Component Interconnect Express) bus, the same high-speed interface used by GPUs. This direct path slashes latency and opens up a much wider data pipe. A single PCIe 4.0 NVMe drive can deliver sequential read speeds of over 7,000 MB/s, more than ten times that of a SATA SSD. When training a model on a large dataset, this difference in data access speed is substantial.
When selecting drives, two metrics are particularly important: bandwidth and IOPS.
The table below illustrates the dramatic differences between storage technologies.
| Storage Metric | HDD (7200 RPM) | SATA SSD | NVMe SSD (Gen4) |
|---|---|---|---|
| Sequential Read | ~150 MB/s | ~550 MB/s | ~7,000 MB/s |
| Sequential Write | ~120 MB/s | ~520 MB/s | ~5,000 MB/s |
| Random Read IOPS | ~100 | ~95,000 | ~1,000,000 |
| Latency | 5-10 ms | 70-100 µs | < 20 µs |
As you can see, NVMe drives offer an order-of-magnitude improvement in every performance category, making them the default choice for the primary storage of any serious AI server.
Using a single drive, even a fast one, is rarely optimal. By combining multiple drives into a RAID (Redundant Array of Independent Disks) array, you can multiply performance, add data protection, or both. For AI workloads, two configurations are most common for your "hot" data tier.
In a RAID 0 configuration, data is "striped" across multiple drives. When a file is written, it is broken into chunks, and each chunk is written to a different drive simultaneously. This multiplies the effective read and write speed by the number of drives in the array (with some overhead). For example, a RAID 0 array with four NVMe drives capable of 7,000 MB/s each could theoretically approach a read speed of 28,000 MB/s.
The major trade-off is the lack of redundancy. If any single drive in a RAID 0 array fails, all data on the entire array is lost. Because of this, RAID 0 is best suited for "scratch" space, where you store temporary data, such as training datasets that are backed up elsewhere or intermediate model artifacts that can be regenerated.
In RAID 0, data blocks are split and written to all drives in the array, maximizing throughput.
RAID 10 provides a balance of performance and protection. It requires at least four drives and works by creating mirrored pairs (RAID 1) and then striping data across these pairs (RAID 0). The result is a system that has the read performance of a RAID 0 array and provides complete data redundancy. If one drive in a mirrored pair fails, the system continues to operate without data loss.
The downside is cost and capacity efficiency. You only get to use 50% of the total raw storage capacity. For example, four 2TB drives in a RAID 10 configuration yield a usable capacity of 4TB. However, for storing valuable models or unique datasets where both performance and safety are important, RAID 10 is an excellent choice.
RAID 10 combines the striping of RAID 0 with the redundancy of RAID 1, offering a mix of speed and data protection.
A practical approach to on-premise AI storage involves creating tiers based on performance needs and cost.
By designing your storage configuration to eliminate I/O bottlenecks, you ensure that your compute resources are used effectively, directly supporting the goal of minimizing total training time and maximizing the return on your hardware investment.
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