Why Enterprise NVMe SSDs Are Critical to Modern AI Infrastructure

Over the past two years, AI has shifted from a "race of model capabilities" to a competition centered on compute and data infrastructure. As vector databases, Retrieval-Augmented Generation (RAG), model training, fine-tuning, and large-scale inference continue to expand, the importance of the storage has been amplified to an unprecedented degree.

Unlike traditional OLTP/OLAP workloads, AI workloads exhibit a "hydraulic-press-like" pressure pattern on storage: intensive random reads, massive sequential reads, continuous sequential writes, ultra-low latency, strict tail-latency requirements, and strong consistency guarantees. For this reason, enterprise NVMe SSDs have become not just a "nice-to-have," but a fundamental requirement for AI system efficiency and stability.

Vector Search: Massive Random Reads and Tail Latency Define the Performance Ceiling of RAG

Modern AI systems often rely on RAG to provide factual grounding and reduce hallucinations. However, these real-world knowledge bases are enormous, constantly updated, multimodal, and-after vectorization-often reach tens or hundreds of billions of vectors, easily spanning tens of terabytes to petabyte-scale in storage.

If Approximate Nearest Neighbor (ANN) indexes must fit entirely in DRAM, systems quickly hit limits: DRAM is costly, difficult to scale, and cannot increase capacity linearly. Once indexes spill beyond DRAM, cache misses surge, triggering latency spikes that directly degrade retrieval quality.

This is why architectures like DiskANN are gaining adoption-offloading large index data blocks to SSD while keeping only the top graph structure and hot data in memory. This approach dramatically improves scalability without compromising accuracy.

Large vector databases therefore depend heavily on SSD behavior, particularly across:
· Millions of small random reads per second
· Sustained high concurrency with virtually no idle windows
· Extreme sensitivity to P99/P99.9/P99.99 latency
· Very strict read-side Quality of Service (QoS) stability

If tail latency spikes-even by 1 ms-vector search response time immediately deteriorates, severely degrading user experience. In real AI deployments, SSD stability matters more than peak benchmarks.

Model Training & Fine-Tuning: Throughput, Latency, Consistency, and Endurance Fully Stress the Storage System

If vector search pushes SSD random-read performance to the limit, then model training and fine-tuning exercise every dimension of an SSD, including sequential/random read throughput, sequential write bandwidth, latency, tail behavior, write amplification, endurance, and-critically-data integrity.

Modern training pipelines must feed massive datasets to multiple GPUs at extreme speeds. As model sizes grow, datasets balloon from tens of gigabytes to tens of terabytes or even petabytes. Data loading itself has become one of the most challenging stages of training.

Different tasks exhibit vastly different data access patterns: some depend on dense small-block random reads, others on high-bandwidth sequential reads. But once multi-GPU training begins, the SSD's read performance is driven to saturation. The more powerful the GPU, the more sensitive the system becomes to read latency and tail spikes-any disturbance forces GPUs to idle, slowing the entire training cycle.

Additionally, during training, systems generate large intermediate files and periodically perform Checkpoints. These writes are often tens or hundreds of gigabytes each and must complete quickly. Any stall or slowdown extends the checkpoint window, wasting GPU time.

Checkpoint integrity is equally critical: a corrupted checkpoint may prevent recovery from the latest training state, forcing a rollback to an earlier version and wasting enormous compute cycles. This is one of the clearest distinctions between enterprise and consumer SSDs.

In short, training and fine-tuning workloads subject SSDs to simultaneous pressure from high random reads, high sequential reads, heavy sequential writes, low latency requirements, strong consistency guarantees, and high endurance demands-a fundamentally different challenge from traditional data-center traffic.

Inference: Low Latency and Multi-Instance Concurrency Make SSD Stability a Core Variable

Inference is often viewed as GPU-centric, but at true production scale, SSD performance becomes critical to multi-instance concurrency, responsiveness, and elastic scaling.

First, model weight loading depends heavily on storage. A 10-70B parameter model typically requires tens of gigabytes of FP16 weights. Larger 100B+ or multimodal models can require hundreds of gigabytes. As a result, instance startup, scaling, and rollout all hinge on SSD sequential and random-read performance.

When dozens or hundreds of instances start simultaneously, storage stability determines cold-start latency and scaling time. If SSD latency becomes unpredictable under load, service replicas take far longer to initialize-directly limiting service elasticity.

Second, inference generates large KV Cache structures. With growing context windows and many parallel instances, total KV Cache easily exceeds GPU memory.

To solve this, many architectures now implement hot-cold KV Cache tiering:
· Hot cache stays in HBM
· Cold cache spills into DDR or high-performance SSDs

Once SSDs become part of the KV Cache tier, their random-access latency, behavior under burst load, tail latency, and QoS stability become mission-critical-especially because KV Cache I/O can represent up to 80% of end-to-end inference time in certain models.

Even a single tail-latency spike produces a visible inference-latency cliff, severely degrading user experience.

Thus, inference performance depends not only on how many tokens GPUs can process, but also on whether SSDs can sustain predictable, stable latency under long-term, high-concurrency pressure.

Power Efficiency: In the Era of 10 kW AI Servers, Storage Efficiency Matters Too

With GPU power skyrocketing, modern AI servers routinely operate at 10,000-watt power envelopes. In this new thermal and energy reality, every subsystem's efficiency matters. SSDs individually consume modest power, but at hyperscale, under constant load, their cumulative impact is significant.

Lower SSD power consumption results in reduced heat output, which not only helps meet the cooling and stability requirements of densely deployed AI servers but also lowers overall power and cooling demand. This, in turn, frees up more of the system's power budget for GPUs, allowing them to sustain higher and more stable Boost clocks during training and inference. Such improvements in storage energy efficiency ultimately have a direct impact on the long-term Total Cost of Ownership (TCO) of AI clusters.

PBlaze7 7A40: A PCIe 5.0 NVMe SSD Purpose-Built for AI Workloads

Designed specifically for AI workloads, the PBlaze7 7A40 delivers leading read and write performance. It reaches 3.3M random-read IOPS and 14.1 GB/s sequential read, providing steady and abundant throughput for dataset loading, KV Cache access, and RAG retrieval scenarios.

On the write side, thanks to extensive hardware acceleration and Memblaze's deep optimizations across the I/O path, hardware scheduling, and FTL algorithms, the PBlaze7 7A40 delivers 1,000K random-write IOPS and 11.2 GB/s of sequential-write bandwidth, making it highly effective for checkpoints, parameter dumps, and logging paths in training workflows.

Latency is equally impressive, with 55 μs read and 5 μs write latency, plus advanced interrupt-coalescing support, reducing CPU overhead and ensuring smooth multi-GPU throughput.

In MLPerf Storage v2.0, the PBlaze7 7A40 shows meaningful advantages in checkpoint tests and delivers stable performance across typical workloads such as Unet3D, CosmoFlow, and ResNet-50, offering a robust and predictable storage backbone for AI systems.

On power efficiency, the 7A40 is optimized to deliver top-tier performance within predictable power envelopes:
≤ 16 W typical random-read power
≤ 13 W sequential-read power
≤ 22 W peak write power

This improves deployability in high-density, high-thermal AI nodes.

Looking ahead, as data density continues to rise, the demand for higher-capacity SSDs becomes inevitable. The PBlaze7 family's upcoming 122.88 TB SKU will enable larger datasets, more complex training pipelines, and higher-dimensional RAG systems-effectively raising the ceiling for next-generation AI infrastructure.

Flash Is Becoming a First-Class Citizen in AI Compute Architecture

AI is reshaping how data is created, consumed, and processed-fundamentally rewriting infrastructure design. As models scale, data access patterns evolve, and clusters expand, storage is no longer a "back-end component" but a core determinant of system capability.

Whether it's the strict tail-latency requirements of vector search, the high-throughput and consistency needs of training, or the concurrency and stability demands of inference-enterprise NVMe SSDs are now a foundational pillar of AI compute.

High performance, strong QoS, consistency, endurance, and energy efficiency will continue making enterprise NVMe SSDs indispensable to modern AI infrastructure. Flash technology will play an increasingly important role in every breakthrough and expansion of next-generation AI systems.

Qiong Wu | PR Manager, Marketing Department
Mobile: +86 15810719739
E-mail: qiong.wu@memblaze.com
Address: B2-A302, Dongsheng Technology Park, No.66 Xixiaokou Road, Haidian District, 100192, Beijing China

Memblaze is the world's leading supplier of enterprise-level SSD (Solid State Drive) products and solutions. The PBlaze series SSD launched by Memblaze has been widely used in database, virtualization, cloud computing, big data, artificial intelligence and other fields, providing stable and reliable high-speed storage solutions for many customers in Internet, cloud service, finance, telecommunications and other industries.

This release was published on openPR.