NVMe for AI Workloads

Sep 7

Introduction

The rapid scaling of artificial intelligence has shifted the industry's attention toward compute; GPUs, accelerators, and interconnect fabrics dominate the conversation. Yet as training clusters grow to thousands of accelerators and inference systems must serve massive contextual memory in real time, storage has quietly become one of the most consequential design decisions in AI infrastructure. Non-Volatile Memory Express (NVMe) technology sits at the center of this shift. This article examines why NVMe has become foundational to modern AI workloads, the technical developments driving its adoption, and the considerations organizations should weigh when architecting AI storage systems.

The Storage Bottleneck Problem

Modern GPUs are capable of extraordinary throughput, but that capability is only realized if data can be delivered to them fast enough. When storage cannot keep pace, expensive accelerators sit idle waiting on data, a condition often described as GPU starvation. This is not a theoretical concern. Training large models increasingly involves processing datasets measured in tens of terabytes, and checkpointing multi-billion-parameter models requires writing and reading hundreds of gigabytes at frequent intervals. Given that a single high-end GPU can cost several dollars per hour to operate, storage-induced idle time translates directly into wasted capital.

Traditional storage architectures were not designed with this profile in mind. CPU-mediated data paths, in particular, introduce latency and consume CPU cycles that would otherwise be available for orchestration and preprocessing. As AI clusters have scaled, this architectural mismatch has become one of the primary constraints on effective GPU utilization.

Why NVMe Is Well Suited to AI Workloads

NVMe was designed from the outset for flash-based storage accessed over PCI Express, using a streamlined command set and massively parallel queuing model, up to 64,000 queues, each supporting up to 64,000 commands, in contrast to the single-queue, deep-latency design of legacy protocols such as SATA and SAS. This parallelism aligns naturally with the highly concurrent, high-throughput access patterns generated by GPU-accelerated training and inference.

Several developments have extended NVMe's relevance specifically for AI infrastructure:

PCIe Gen5 and Gen6 bandwidth. Enterprise NVMe SSDs built on PCIe Gen5 now deliver sequential throughput in the range of 14 GB/s per drive, and early PCIe Gen6 datacenter drives are beginning to reach production, with vendors reporting sequential read speeds approaching 28 GB/s and random read performance in the millions of IOPS. This bandwidth growth allows a modest number of drives per node to feed data-hungry accelerators without requiring exotic architectures.
GPUDirect Storage (GDS). NVIDIA's GPUDirect Storage technology allows data to move directly between NVMe storage and GPU memory via direct memory access, bypassing the CPU and system memory entirely. This removes a longstanding bottleneck in training pipelines, where continuous data ingestion is essential to keep GPUs saturated, and recent refinements have pushed even the last remaining CPU involvement out of the data path.
NVMe over Fabrics (NVMe-oF). As AI clusters have grown, the ability to disaggregate storage from compute, while preserving NVMe's low-latency characteristics across the network, has become increasingly important. NVMe-oF, running over RDMA-capable networks such as InfiniBand or RoCE, allows centralized storage pools to deliver performance approaching that of local drives, enabling more efficient hardware utilization and simplified scaling.
KV cache offload for inference. As reasoning and agentic inference workloads generate increasingly large key-value caches, GPU high-bandwidth memory alone is no longer sufficient to hold context economically. NVMe is emerging as a persistent tier in multi-level cache hierarchies for inference frameworks, extending effective context capacity across GPU memory, host memory, and flash storage without materially compromising response latency.

Architectural Considerations

Organizations building or upgrading AI infrastructure should weigh several factors when incorporating NVMe into their storage strategy:

Balance across the pipeline. Storage throughput is only valuable if it is matched by adequate PCIe lane allocation, network bandwidth, and CPU capacity for orchestration. An unbalanced system simply shifts the bottleneck elsewhere.
Local versus disaggregated storage. Local NVMe offers the lowest latency and is often sufficient for checkpointing and moderate working-set sizes. Disaggregated NVMe-oF architectures offer better utilization and easier scaling for larger, shared datasets, at the cost of additional network infrastructure and complexity.
Data shard sizing and access patterns. AI training pipelines benefit from careful attention to object and shard sizing, queue depth configuration, and caching tiers, since these parameters materially affect achievable throughput at scale.
Total cost of ownership. High-performance NVMe deployments carry a meaningful capital cost, but must be evaluated against the cost of idle accelerator time, which for large clusters can quickly exceed the incremental cost of faster storage.

Looking Ahead

The trajectory is clear: as model sizes, context windows, and cluster scales continue to grow, the demands placed on the storage layer will only intensify. Continued advances in the NVMe specification, PCIe bandwidth, and direct GPU-storage data paths suggest that NVMe will remain the default building block for AI storage for the foreseeable future, even as the surrounding architecture, disaggregation, fabric-attached pools, and multi-tier caching, continues to evolve.

For technology leaders evaluating AI infrastructure investments, storage should no longer be treated as a secondary consideration behind compute procurement. It is an integral part of the system that determines whether costly accelerator investments deliver their expected return.

References

Dhupinder Malhotra