Why CAE and EDA performance increasingly depends on memory, data movement, power, cooling, and cloud capacity.
For the past several years, the conversation around engineering infrastructure has been dominated by accelerators. Every conference, roadmap briefing, and artificial intelligence (AI) announcement seems to reinforce the same message: more graphics processing units (GPUs), bigger clusters, faster innovation.
Yet many engineering organizations are discovering a different reality. They invest in the latest compute technologies but still find engineers waiting for jobs to start, simulations to complete, datasets to load, or design databases to move across the network. Despite major advances in processing power, end-to-end engineering productivity often improves less than expected.
The reason is simple; performance has become a systems problem. For electronic design automation (EDA) and computer aided engineering (CAE) organizations, the next bottleneck is increasingly not the processor. It is everything around it.
Engineering Workloads Are Changing Faster Than Infrastructure
EDA, CAE, AI, and digital engineering workloads have become dramatically more demanding. Design databases continue to grow. Verification regressions consume increasingly more compute resources. Computational fluid dynamics (CFD) and multiphysics simulations use larger meshes and more complex physics. AI is also becoming more deeply integrated into design, simulation, optimization, and verification workflows.
Yet many infrastructure environments still reflect design assumptions developed when compute was the primary limitation. Today, some of the biggest delays occur before a processor even starts working. Engineers wait for files to load. Data is transferred between storage tiers. Jobs sit in queues waiting for memory or capacity. Results are checkpointed and written back to storage. AI workflows spend significant time feeding datasets to accelerators. The entire infrastructure path increasingly matters more than any single component.
Data Movement Is Emerging as the New Performance Metric
Consider a modern engineering workflow. A modern engineering workload begins by accessing design data, models, libraries, or simulation inputs. The workflow may then stage data from storage, transfer information across the network, move it into memory, exchange messages between compute nodes, write checkpoints, and finally store the results. Only part of that workflow consists of actual computation.
As datasets continue to grow, data movement increasingly determines total job turnaround time. This trend is also reshaping storage architectures. Industry analysts increasingly describe storage as evolving from a passive repository into an active data platform capable of supporting simulation, AI training, AI inference, and emerging AI-enabled and agentic engineering workflows simultaneously. Traditional storage strategies optimized primarily for checkpoint and restart operations are becoming less effective as workload diversity increases. For many engineering teams, reducing data movement can produce greater productivity gains than purchasing the next generation of processors.
The GPU Conversation Is Shifting
The good news is that GPU adoption is continuing to expand beyond national laboratories and elite supercomputing centers. Accelerated computing is moving into divisional, departmental, and workgroup environments, making it increasingly accessible to engineering organizations of all sizes. The business case is becoming clearer for many AI, simulation, and optimization workloads.
The challenge is that not every workload benefits equally. Some EDA and CAE applications achieve substantial acceleration. Others remain limited by memory capacity, data movement, solver characteristics, or software architecture.
The question is therefore not simply, “How many GPUs should we buy?” It is, “Which workloads benefit from acceleration, and what limits their scaling?”
Organizations that answer that question first typically make better investment decisions.
Memory Is Emerging as a Strategic Concern
While GPUs attract headlines, memory is quietly becoming one of the most important design considerations in engineering infrastructure. Larger models, designs, and simulation datasets place growing pressure on memory systems.
In many environments, the challenge is no longer performing computations. The challenge is keeping the right data close enough to compute to sustain utilization.
This trend is compounded by ongoing supply constraints. Hyperion Research notes that organizations planning new infrastructure purchases continue to face significant availability and pricing challenges for GPUs, central processing units (CPUs), and memory, with vendors locking in supply agreements years in advance. As a result, memory planning is increasingly becoming a strategic exercise rather than simply a server configuration decision.
Power Is Becoming the New Capacity Constraint
One of the biggest constraints may not be silicon. It may be electricity. Hyperion Research reports that obtaining sufficient power to support next-generation AI and HPC infrastructure remains a major concern, particularly in larger metropolitan areas where utility capacity is increasingly constrained. Many existing facilities cannot support the power levels required by next-generation systems.
This creates an unusual situation. An engineering organization may have budget approval for additional infrastructure yet still be unable to deploy it because of facility limitations. The consequences are already visible across the industry. Liquid cooling is becoming more common, power planning is moving higher on IT agendas, and infrastructure decisions increasingly involve facilities teams alongside computing specialists. For organizations planning future data centers, power should now be evaluated as carefully as processor performance.
Why More Engineering Workloads Are Moving to the Cloud
Another consequence of supply-chain and power constraints is accelerating cloud adoption. Hyperion Research notes that cloud adoption for HPC and AI workloads continues to grow, partly because hyperscalers often have better access to supply allocations and power resources than enterprise data centers. Users are increasingly using cloud infrastructure while delaying on-premises upgrades.
The trend is particularly visible in EDA. Hyperion forecasts EDA cloud spending to grow from roughly $891 million in 2025 to more than $2.2 billion by 2030, representing about a 20% annual growth rate. At the same time, EDA on-premises spending continues to grow, but at a slower pace of approximately 13% annually.
This is an important distinction. The market is not signaling the end of on-premises infrastructure. Instead, it suggests that cloud is increasingly becoming an extension of engineering capacity. The practical goal is therefore not to choose between cloud and on-premises infrastructure, but to place each workload where it delivers the best combination of performance, capacity, security, and economics.
The New Goal: Eliminating Bottlenecks
The organizations that benefit most from EDA, simulation, and AI will not necessarily have the largest clusters or the most GPUs. They will understand where their engineers are waiting and remove those bottlenecks systematically.
Modern engineering performance depends on how effectively compute, memory, storage, networking, power, cooling, and data management work together. As you evaluate your next infrastructure investment, focus less on peak component specifications and more on end-to-end engineering productivity.
To continue the conversation, visit us at Booth 1727 at Supercomputing 26. We would be pleased to discuss how CAE and EDA organizations are preparing their infrastructure for the next generation of engineering workloads.
