In today's AI infrastructure, storage is often divided between high-performance TLC SSDs and high-capacity QLC SSDs. TLC drives handle tasks like training, fine-tuning, and inference, while QLC SSDs support data lake with cost-efficient density. This role split has become the norm.

But as compute density increases — especially with modern GPU deployments — TLC SSDs are taking on more than just the “hot tier.” Memblaze's PBlaze7 7940 PCIe 5.0 SSD exemplifies this shift.

Speed, Capacity, and Efficiency — No Compromises
 
Among the world's first PCIe 5.0 enterprise SSDs, the PBlaze7 7940 delivers up to 14GB/s sequential reads, 10GB/s writes, and up to 30.72TB capacity — all while consuming just 16W in typical workloads. It challenges the long-standing belief that you must compromise performance for capacity, or power for performance.

The PBlaze7 7940 has already seen widespread adoption by leading AI and internet companies, with hundreds of thousands of units deployed. It stands out as a TLC SSD capable of serving multiple roles across the AI workflow.

Smarter AI at the Edge — From Preprocessing to Real-Time Inference

AI pipelines begin with massive volumes of data — from endpoints such as cameras, lidars, smartphones, cash registers, and other devices. However, centralized preprocessing often leads to network bottlenecks and latency, limiting the efficiency of downstream AI tasks.

A more effective approach is to move preprocessing closer to where the data is generated. By deploying compute units at the edge, raw and unstructured data collected from endpoints can be filtered, cleaned, and reduced in size before being transmitted. This not only alleviates data transfer pressure across the network, but also ensures faster responsiveness. Such workloads, however, place high demands on local SSDs, particularly in terms of sustained write performance and mixed read/write capability.

The PBlaze7 7940 E1.S addresses these challenges directly. With up to 15.36TB capacity per drive and write performance nearly three times that of mainstream QLC SSDs in sustained and mixed workloads, it enables edge servers to integrate more storage and compute within a compact footprint. The result is higher throughput and improved reliability for edge-side data preprocessing.

In latency-sensitive applications such as autonomous driving, smart retail, and security monitoring, edge nodes can aggregate local data streams and optimize decision-making in real time. By combining powerful preprocessing capability with enterprise-grade storage, edge infrastructure becomes a strong enabler for more efficient AI execution and better business outcomes.

Scaling AI in the Data Center — Training, Checkpointing, and Inference

While edge nodes play a vital role in preprocessing, the data center and cloud remain the backbone for AI training and large-scale inference. Here, high-performance and high-capacity SSDs are critical to sustaining throughput and maximizing GPU utilization.

For training, fast access to massive datasets is essential. High-bandwidth SSDs ensure that terabytes or even petabytes of training data can be rapidly streamed into system memory or GPU HBM, minimizing idle cycles and accelerating time-to-train. Equally important is checkpointing: large models require frequent saving of intermediate states. With faster, more reliable SSDs, checkpoints can be written with minimal disruption, and training jobs can be quickly resumed after interruptions — reducing both cost and risk.

In inference workloads, particularly multi-round interactions and long-context tasks such as LLM-powered dialogue or code generation, storage also plays a key role. High-capacity SSDs can be used to extend KV cache, reducing the burden on system memory and enabling smoother, more consistent performance for long-sequence inference.

The PBlaze7 7940 U.2 30.72TB delivers these advantages at scale. Offering two to four times the capacity of mainstream high-performance TLC SSDs, it allows compute nodes to host larger datasets and more checkpoints locally, minimizing reliance on remote storage systems. With fewer SSDs needed to achieve the same storage footprint, valuable server space and PCIe resources can be redirected to additional GPUs or high-speed network interfaces — directly boosting compute density and cluster efficiency.

In short, by combining ultra-high capacity with enterprise-grade performance, the PBlaze7 7940 helps data centers remove storage bottlenecks and fully unleash the potential of AI training and inference.

Reshaping the Role of TLC SSDs in the AI Era

From the edge to the data center, AI is pushing storage to do more than ever before. What was once a clear division — TLC for performance, QLC for capacity — is now being redefined. The PBlaze7 7940 proves that a single TLC SSD can deliver uncompromising speed, density, and efficiency, enabling it to serve multiple roles across the AI pipeline.

By breaking through long-held limitations, the PBlaze7 7940 doesn't just support AI workloads — it reshapes the very role of TLC SSDs in the AI era, positioning them as a foundation for faster innovation and smarter infrastructure.