When network performance drops, the instinctive response is to add more memory. However, in many modern data center environments, memory is often assumed to be the constraint even when workloads are limited elsewhere. In distributed systems, compute resources can remain underutilized because data cannot move through the network quickly enough to keep central processing units (CPUs) and graphics processing units (GPUs) busy.
At the same time, AI workloads, high-density virtualization, and data-intensive applications drive demand for dynamic random-access memory (DRAM). High-bandwidth memory (HBM) production is absorbing a growing share of manufacturing capacity, reducing standard Dual In-line Memory Module (DIMM) availability and pushing prices higher. As a result, DRAM contract prices are rising as supply tightens.
As memory costs increase and returns diminish, the more productive question is where performance is constrained: by local memory capacity, or by the ability to move data efficiently across the network?
In this article, we discuss the issue of solving the wrong fix. We cover why rising memory prices are only part of the story, the implications of missing network performance constraints, and how AddOn helps identify and solve the real blockers at the network layer.
The Capacity Crunch: Compute Stalls From Network Underperformance
Across AI and virtualized infrastructure, operators report a familiar pattern: CPUs and GPUs remain underutilized even as workloads slow and queues build. In fact, despite this year’s predicted $300 billion AI hardware spend, more than 75% of organizations are running their GPUs below 70% utilization. Even at peak times.
The common assumption is that memory is the constraint. However, distributed workloads rely on constant, high-speed data exchange between nodes, storage, and accelerators. East–west traffic grows, pushing network fabrics to their limits long before compute or memory capacity is exhausted.
This mismatch explains why many AI and machine learning environments struggle to achieve high accelerator utilization during peak workloads. The issue isn’t a lack of DRAM. It’s that data arrives too slowly to keep compute engines busy. Unsurprisingly, 69% of senior IT leaders and 66% of CTOs say their current network infrastructure does not have the capacity to embrace generative AI to its full potential.
But when performance stalls, the first instinct for many teams is to add memory. This response often targets the wrong layer of the stack.
Memory Expansion? A High-Cost, Low-Return Move
DRAM economics have changed significantly. Pricing has become volatile as AI accelerators reshape memory manufacturing priorities. HBM absorbs disproportionate wafer and packaging capacity, tightening supply for standard DIMMs even when overall demand remains strong.
Hyperscalers and large AI deployments also consume more DRAM per server, intensifying competition for supply. This combination is increasing prices and lead times while causing unpredictable swings in availability.
Memory upgrades are expensive and deliver limited performance improvements when workloads are already constrained. Adding DRAM does not fix stalled pipelines if data movement remains the limiting factor. For operators, the only thing this “fix” offers is poor returns.
Storage Costs Add Pressure, Not Relief
Memory is not the only component under strain. NAND flash pricing was expected to rise 5–10% in late 2025 as manufacturers shift production toward higher-value products, pushing Solid-State Drive (SSD) costs up.
The market expects 2026 NAND demand to grow 20%–22% YoY, with supply projected to rise only 15%–17%, widening the supply-demand gap. Hard disk drive (HDD) availability has also tightened due to consolidation and increased demand for nearline capacity, with manufacturers reporting that production lines are almost fully utilized.
These rising costs make component-led performance upgrades even more difficult to justify. When every layer becomes more expensive, improving performance requires a more targeted approach. One that addresses the true source of inefficiency. In modern environments, that source is the network.
The Real Bottleneck: Network Saturation
As workloads become more distributed and parallelized, data must move rapidly across clusters to avoid idle compute cycles. So, when bandwidth falls short, performance degrades and latency increases, regardless of how much memory is installed. Network congestion introduces delays in data loading, synchronization, and I/O operations. CPUs and GPUs spend more time waiting than processing. In these conditions, expanding DRAM simply increases the size of the queue behind the bottleneck rather than removing it.
The solution lies in accurately identifying the dominant constraint. In many modern, distributed workloads, that constraint is data movement across the network rather than local memory capacity.
The shift toward east–west traffic means network design is now central to maintaining system performance. Ignoring this leads to misdirected spend and persistent underutilization.
Fiber Upgrades Deliver Strong ROI
In environments where workloads are constrained by data movement rather than local working set size, increasing network capacity delivers more immediate performance gains than expanding memory alone.
Upgrading to higher-bandwidth fiber reduces congestion and improves latency consistency. Faster data movement across nodes translates into higher CPU and GPU utilization, which in turn shortens job completion times and delivers better overall throughput.
From a cost perspective, optics and memory behave differently. DRAM and NAND pricing is highly cyclical and can swing with supply reallocations and AI-driven demand. Optical transceiver economics vary by tier: mature, high-volume transceivers often follow predictable cost-down curves, whereas leading-edge speeds can face supply tightness and pricing pressure during rapid adoption cycles.
In practice, this makes network upgrades easier to plan at mature speeds and makes early qualification and supply-chain options important for next-gen deployments.
High-Performance Optics Without the OEM Premium
Strengthening the network fabric helps operators get more from existing compute while limiting exposure to highly volatile upgrade paths. AddOn Networks supports a smarter upgrade path to help businesses achieve this.
As a strategic extension of your network engineering team, we deliver OEM-compatible optical transceivers engineered to meet strict performance and reliability requirements. Minus the premium pricing attached to branded optics.
As one of the world’s largest suppliers of optical transceivers, AddOn leverages global supply chain depth and multi-source manufacturing to help customers maintain consistent availability—even amidst component volatility.
With one of the industry’s broadest optical portfolios, we enable organizations to scale network capacity in line with workload growth. Our highly skilled engineering support helps operators choose the right optical path without overspending.
For modern data centers evolving across multiple technology generations, extensive interoperability testing across switches, routers, and network interface cards ensures consistent behavior in mixed-vendor environments.
When it comes to qualification, our processes go beyond baseline compliance. Full code verification, environmental stress testing, and ongoing platform validation help ensure long-term reliability.
Our extensive networking connectivity portfolio includes compatibility for both legacy and cutting-edge technologies. The outcome is a dependable way to expand network capacity, allowing you to control cost and risk.
Solve the Right Problem First
DRAM pricing volatility is likely to continue as AI adoption reshapes the memory market. Storage costs follow a similar trajectory, adding uncertainty across the component landscape. Yet in many environments, performance limitations stem from constrained data movement across the network rather than insufficient memory.
To avoid costly misdiagnosis, performance optimization must start by identifying where workloads stall (whether in local memory, storage, or network transport) and addressing the dominant constraint. When the network is the limiting factor, increasing fiber capacity can improve utilization and increase throughput, extending the life of existing hardware.
Memory expansion remains effective when working sets exceed local capacity, but many modern, distributed workloads stall elsewhere. Determining whether performance is limited by local memory or by network-level data movement is a critical first step before investing in additional DRAM.
Time to Put Your Network First.
If your data can’t move fast enough, more DRAM won’t help. Talk to AddOn about fiber upgrades that boost utilization and reduce latency, with high-performance networking minus the OEM premium.