Unclogging the AI Data Pipeline—Three Critical Bottlenecks Constraining AI Clusters

THE HIDDEN COST OF LATENCY SPIKES

Latency is not merely a performance metric—it’s a direct contributor to cost and inefficiency. GPUs are extremely expensive resources; every millisecond they wait for data is a millisecond of wasted capital investment. AI models depend on rapid data availability across memory and storage tiers.

Latency spikes not only stall training and inference but also amplify cascading failures such as memory overflows or lost training epochs.

High tail latency—where a small percentage of requests experience unusually long delays—is especially detrimental in AI workloads where parallel tasks must synchronize frequently.

If just one node experiences slow data retrieval, the entire process can be delayed. As DriveNets notes, these issues aren’t just local but can manifest across distributed AI clusters, compounding delays and reducing throughput.

COMPUTE EXPRESS LINK (CXL): BREAKING THE MEMORY WALL

CXL is an open standard designed to enable high-speed, low-latency connections between CPUs and memory, storage-class memory, and accelerators. Unlike traditional DRAM expansion models that are socket-bound, CXL allows memory to be scaled independently of CPUs. This enables systems to increase memory capacity without additional compute nodes.

With CXL Type 3 memory modules, enterprises can pool or tier memory resources, reducing redundancy and underutilization.

For AI workloads, this means models that previously required distributed training across multiple servers can now be run on fewer, denser nodes with ample memory.

In a 2024 paper, researchers demonstrated that adding CXL memory expansion to AI systems improved effective memory bandwidth by up to 39% and delivered up to 24% performance improvement across common training benchmarks.

CXL-based memory modules connect over the PCIe interfaces rather than connecting directly to the CPU’s memory controller links. Using the more abundant PCIe lanes enables scaling memory capacity and bandwidth far beyond what traditional CPU-attached DIMMs allow.

Connecting memory over PCIe lanes does introduce new challenges in reliability and data integrity. Including advanced ECC (error correction code) features in the CXL controllers improves reliability and enables higher memory density.

NVME SSDS: REDEFINING STORAGE FOR AI SCALABILITY

NVMe SSDs, particularly those integrated directly into server nodes, have revolutionized storage I/O in AI systems.

Unlike older SATA or SAS drives, NVMe drives connect via PCIe, offering significantly higher throughput and lower latency. This allows AI applications to fetch data quickly without traversing congested network links.

Advanced NVMe SSDs now incorporate hardware state machines to perform transparent compression and write reduction.

This reduces the number of physical writes, extending drive life and improving write throughput—critical for AI workloads with high checkpointing and logging frequency.

For instance, compressed storage can multiply effective capacity and write performance by 2–4x without software overhead, leading to both CapEx and OpEx savings.

Furthermore, local SSDs mitigate “I/O storms” during synchronized writes. By placing data closer to the processor, NVMe SSDs reduce the time and energy cost of moving data across switches and routers.

INTEGRATING CXL AND NVME FOR MITIGATING NETWORK BOTTLENECKS

While CXL addresses the memory side of the bottleneck, NVMe addresses storage. Together, they provide means for helping to mitigate the network bandwidth and latency challenges to pave a path forward for scalable, efficient AI infrastructure:

• CXL allows memory to scale independently, eliminating the need for GPU/CPU overprovisioning.
• NVMe ensures that data is delivered to the processor with minimal delay, maximizing accelerator utilization.
• Checkpointing and recovery are accelerated, reducing job failure risk and improving productivity.
• Power consumption is reduced through better locality and lower memory pressure, enabling more compute per watt.

CXL and NVMe scale local data capacity, reducing the need for fetching data across the network and mitigating sensitivity to network latency spikes. This synergistic approach enables data architects and infrastructure teams to build AI systems that are balanced, responsive, and cost-effective.

A STRATEGIC PATH FORWARD FOR IT LEADERS

As AI infrastructure grows in importance, IT executives must look beyond raw compute performance. The real battle is in the data pipeline—balancing the capacities and bandwidth for moving data through the memory, storage, network, and compute elements. Organizations that fail to address the potential bottlenecks throughout their AI and data center infrastructure will face escalating costs and missed opportunities.

CXL and NVMe are key elements in a new generation of infrastructure technology that can help improve efficiency while scaling computing capacity. Their adoption won’t eliminate the challenges of AI infrastructure, but it will provide a foundation for adaptable and sustainable growth.

The AI race will not be won by those who spend the most, but by those who build the smartest, most efficient systems.

Understanding and investing in advanced memory and storage architectures are no longer optional—they are competitive imperatives.