The demand for processing, storing, and moving vast amounts of data has reached unprecedented levels, driven by artificial intelligence (AI), scientific simulations, and data-intensive analytics. Across industries—from high-performance computing and data centers to manufacturing and heavy industry—organizations are hitting the limits of traditional infrastructure. The central challenge is no longer just about raw power; it is about balancing capacity, performance, efficiency, and cost. Modern equipment, however, is rewriting the rules of what is possible.
The Capacity Crunch: A Widespread Problem
High-capacity applications present a variety of challenges that depend on the specific domain.
In high-performance computing (HPC), many critical scientific applications—particularly those integrating AI with computational chemistry—have become “orphaned” by architectures that favor processing speed over large memory access. These applications require massive, directly addressable memory pools that traditional distributed systems struggle to provide. Similarly, in cloud data centers, the memory subsystem dominates both costs and power consumption, with companies spending billions on memory while AI workloads drive demand ever higher.
Data storage itself has become a bottleneck. While storage performance has grown 1000x with the adoption of NVMe SSDs, CPU performance has not kept pace, creating an unbalanced architecture that struggles with database, analytics, and AI/ML workloads. Even when storage devices are pooled together, significant capacity remains underutilized due to performance variability across devices.
These challenges extend beyond the digital realm. In industrial manufacturing, massive injection molding machines—some exceeding 4,000 tons of clamping force and weighing nearly 400,000 pounds—must process enormous parts while minimizing scrap, where a single defective piece can represent a staggering financial loss. Likewise, off-highway vehicles like 40-tonne excavators require battery systems of 400kWh or more, with duty cycles that can run 24 hours a day.
Memory Innovation: Breaking the Bottleneck
One of the most promising developments in addressing high-capacity application challenges is the emergence of new memory architectures. The Crete prototype system, co-designed by Pacific Northwest National Laboratory and Micron, delivers 15 terabytes of active memory co-located with system processors using Compute Express Link (CXL) technology. This architecture provides a unique mix of tightly coupled and loosely coupled memory, enabling data-driven scientific computing that was previously impossible.
Crucially, Crete’s design allows memory sitting outside the network to be easily accessed by multiple hosts simultaneously, passing information without going to the hard drive for large workloads. This represents a fundamental shift from decades of HPC focus on distributed memory system architectures.
For data centers, transparent memory offloading solutions like Meta’s TMO system demonstrate that intelligent memory-tier management can deliver dramatic savings. By using a new metric called pressure stall information (PSI) to track when jobs are not making progress due to resource constraints, TMO dynamically provisions memory across tiers—from fast DRAM to compressed memory and SSD backends—without requiring application changes. Deployed across millions of servers, this approach has proven remarkably effective.
Storage That Adapts in Real-Time
Modern storage solutions are moving beyond static configurations to intelligent, adaptive systems. MIT researchers developed Sandook, a software-based system that tackles three major sources of storage performance variability simultaneously: differences in SSD age and capacity, read-write interference, and unpredictable garbage collection delays.
Using a two-tier architecture with global and local controllers, Sandook can nearly double performance on realistic tasks like AI training and image compression compared to traditional approaches. Remarkably, it pushes SSDs to achieve 95% of their theoretical maximum performance without requiring specialized hardware.
For edge intelligent workstations, new hardware-pass-through remote storage access frameworks like Leopard eliminate complex software stacks that previously caused up to 75% performance degradation. By implementing remote storage access operations as hardware circuits, these solutions deliver up to 6x lower latency for realistic workloads.
Hardware accelerators such as the Pliops Extreme Data Processor (XDP) are also gaining traction. These PCIe-based cards offload computationally intensive storage tasks from the CPU, offering up to 10x performance improvements, 6x greater storage capacity through inline compression, and 7x extended SSD endurance—all without requiring application changes.
New Approaches to Data Movement and Cooling
The challenge of moving massive datasets is being addressed through different networking strategies. Cisco’s new AI networking system, featuring the Silicon One P200 chip and 8223 router, supports 51.2 terabits per second for AI workloads. Its “deep buffering” approach uses large data buckets to smooth bursty traffic flows and prevent packet loss, a critical requirement since dropped packets can cause AI training jobs to fail and require restarting.
The AI data center boom is also driving unprecedented demand for cooling solutions, with the cooling sector expected to grow approximately 37% annually. Modern cooling approaches include cold-plate liquid cooling (becoming the norm for AI data centers) and immersion cooling, which is gaining wider deployment. Manufacturers of cooling equipment are collaborating more closely with data center developers to determine future-proof systems for individual facilities.
Industrial Manufacturing: Scale and Intelligence
In heavy manufacturing, modern equipment combines massive scale with data-driven intelligence. Evco Plastics, running injection molding machines with clamping forces exceeding 4,000 tons, has deployed data analytics to monitor processing outcomes in real time, correlating conditions like water consumption, temperatures, and pressures. This approach has reduced scrap to 2% at their most-connected plants—a significant achievement given the daunting cost of scrapping parts as large as 60 tons.
Similarly, in hydraulic forging presses, new variable displacement pumps paired with variable frequency drives (VFDs) are revolutionizing energy efficiency. By enabling on-demand flow and speed rather than running continuously at constant RPM, these systems unlock substantial energy savings and reduce maintenance costs. Data collection through sensors enables condition monitoring and predictive maintenance, bringing Industry 4.0 capabilities to heavy equipment.
The Path Forward
Across all these domains, several common themes emerge. First, there is a growing recognition that simply throwing more hardware at problems is neither sustainable nor cost-effective. Second, intelligent, adaptive systems that respond to real-time conditions consistently outperform static approaches. Third, specialized hardware acceleration—whether for memory management, storage offloading, or networking—is becoming essential as general-purpose CPUs reach their limits.
Perhaps most significantly, these modern solutions increasingly prioritize sustainability alongside performance. From reducing infrastructure footprints and carbon emissions to extending equipment life through better maintenance, the industry is moving toward what Volvo Construction Equipment’s sustainability leaders describe as balancing “social, environmental and economic sustainability”.
The challenges of high-capacity applications are formidable, but modern equipment and intelligent system design are providing the tools to overcome them—unlocking new possibilities in scientific discovery, industrial productivity, and data-driven innovation.
