HPC and AI converging infrastructures

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HPC and AI converging infrastructures Opinion By Vik Malyala published 11 December 2025

The boundaries between HPC and AI workloads becoming increasingly blurred

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The modern data center is undergoing a significant transformation driven by increasing complexity and widespread integration of artificial intelligence (AI).

This change is characterized by the convergence of high-performance computing (HPC) and AI workloads, which is driving innovation at every layer of the infrastructure from chip design to cooling systems.

Vik MalyalaSocial Links Navigation

Managing Director & President for EMEA at Supermicro.

The size of today's deep learning models, especially large language models (LLMs) and foundation models, requires computational resources previously used for advanced scientific supercomputing. This is pushing AI into the HPC domain in terms of infrastructure.

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Rethinking compute and architecture

This convergence represents a significant shift in how data center designers use and configure processors. In the past, data centers mainly depended on general-purpose, multi-core central processing units (CPUs) to perform many tasks.

While CPUs are still vital for some sequential or low priority tasks, the parallel nature of modern AI algorithms, such as neural network training, requires specialized hardware. As a result, more advanced graphics processing units (GPUs) have become the preferred hardware for AI workloads.

The growing complexity of AI models, which can involve billions or even trillions of parameters, requires high-performance parallel processing on an unprecedented scale.

This has fundamentally changed data center architecture, accelerating the adoption of multi-GPU systems and advanced accelerators. Simply installing many GPUs in a rack isn’t enough; they must communicate with each other seamlessly and quickly.

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This has led to the development of advanced interconnect technologies, such as high-speed InfiniBand, as well as specialized Ethernet fabrics, which provide low-latency, high-bandwidth communication pathways essential for efficient collective operations during distributed training.

The performance of these interconnects often determines the overall scaling and training time for large AI models.

The new infrastructure considerations: Power, storage, and cooling

The shift to high-density GPU clusters has introduced significant engineering challenges, especially in power and thermal management. The compute and power density in AI/HPC racks far exceed those of traditional enterprise racks, leading to higher rack-level power demands.

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This has prompted data center designers to reevaluate power distribution units (PDUs) and uninterruptible power supplies (UPS), focusing on higher-voltage, more efficient power-delivery systems.

Many varied demands of AI workloads require tailored infrastructure. Training large models involves supplying massive, often terabyte-scale, datasets to accelerators at very high speeds to prevent GPU starvation.

This results in the use of various high-performance storage options, such as parallel file systems supported by flash storage (NVMe solid state drives). This approach ensures that the input-output (I/O) subsystem does not become a bottleneck, thereby maximizing the efficiency of expensive computing resources.

An essential component of this convergence is the issue of how to cool the systems. Air cooling struggles to remove the massive heat generated by modern high-TDP (thermal design power) accelerators.

Technologies such as direct-to-chip liquid cooling and immersion cooling are transitioning from HPC applications to mainstream AI data center use, offering improved energy efficiency and enabling much higher rack densities.

Scalability, modularity, and future-proofing

Organizations are quickly expanding their AI initiatives to boost productivity and operational efficiency. To support this continuous growth, infrastructure investments need to be adaptable and future-proof leading to an increased preference for flexible and modular server designs.

These systems enable businesses to manage energy use and space more cost-effectively. Since AI workloads constantly evolve, the ability to easily upgrade or expand compute and storage components, without costly infrastructure overhauls, provides a vital competitive edge and helps lower the total cost of ownership.

Many of the new technologies needed by an AI data center originally came from the HPC field. IT managers responsible for installation must look beyond the performance of individual servers and view the entire system as a single, parallel machine, where all components work together.

This involves carefully analyzing network topology to reduce latency and increase bisection bandwidth, and examining storage components to ensure they handle the I/O demands of the accelerators.

By aligning their current capabilities, incorporating necessary third-party infrastructure where proprietary solutions don’t work, and customizing infrastructure design to match the expected nature of future AI workloads, businesses can achieve an optimal balance.

This strategic alignment is essential for leveraging the power of HPC-AI convergence, allowing organizations to expand their innovation without incurring excessive costs.

Summary

A new type of data center is emerging, designed explicitly for AI workloads. Many of the optimizations developed over the years for HPC data centers are now being applied to AI data centers.

Although some hardware components may differ depending on the workload, there are valuable lessons to learn and experiences to be transferred.

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TOPICS AI Vik MalyalaSocial Links Navigation

Managing Director & President for EMEA at Supermicro.

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