As AI-driven innovations continue to transform industries, the global competition to build the world’s most powerful supercomputers has gained significant momentum, with contenders located in China, the US, and the EU.
At the same time, investments in energy to power computer resources have become a priority for companies interested in AI development, deployment or financing. Yet, despite this surge in technological advancements, the scale and impact of supercomputing often remains elusive and underestimated, both on a technical level and as a societal phenomenon.
This article breaks down the definition of supercomputing and explores how companies can maximize their AI investments by leveraging solutions powered by the speed and scale of a supercomputer.
What is a supercomputer?
Supercomputers serve as the backbone of high-performance computing, driving AI models capable of processing massive amounts of data at scale and at incredible speed. It is an advanced computing system made of multiple high-performance hardware components. Working together, these components process immense volumes of data, execute complex computations, and are essential for tasks that require substantial computing power, such as physics simulations, scientific research, and weather forecasting. These supercomputer hardware components include:
- High-capacity RAM (petabytes) for rapid data access
- Advanced storage solutions (SSDs) for fast reading and writing speeds.
- Multi-core processors (CPUs and GPUs) for parallel computing
- High-speed network interconnects to synchronize processing across nodes
- Specialized cooling systems to manage heat dissipation and prevent Joule overheating.
Building a supercomputer requires a substantial investment, often reaching hundreds of millions of dollars, along with ongoing maintenance and operational costs. In 2023, IBM announced a 10-year quantum supercomputer deal amounting to $100 million, and companies like Microsoft reportedly plan on building a $100 billion supercomputer to run its AI models.
What are the benefits of supercomputing?
Supercomputers power advanced AI models and applications, optimizing efficiency and performance across multiple domains, notably:
1. Accelerating innovation cycles
Supercomputers can run multiple simulations and experiments in parallel, which significantly reduces the time required for research and development. This capability is particularly valuable in sectors such as pharmaceuticals, automotive and materials science, where fast and efficient prototyping can lead to breakthrough innovations.
2. Managing high-complexity computations
Supercomputers can process large datasets and handle complex calculations, solving problems with large amounts of FLOPs (floating point operations per second). As such, supercomputing capabilities greatly benefit industries that require computational power to handle massive datasets, such as financial modeling, genomic sequencing, and AI training.
Supercomputing for industry-specific use cases
Computers that include more hardware are particularly interesting for industry use cases in need of large, resource-intensive models that can perform at scale, faster. With supercomputers – more hardware and therefore more computing power – the training of AI models takes less time. Consequently, this extra amount of computational power can optimize specific use cases, as per the following examples:
1. High-complexity simulations (R&D, manufacturing, energy)
Supercomputers excel at running advanced simulations for targeted industries because they can perform multiple actions at once, faster. For instance, they may simulate product behavior to refine designs in the automotive sectors or run molecular simulations to accelerate drug discovery, shortening timelines from years to months. Simulation may also optimize wind turbines and maximize efficiency by modeling fluid dynamics in oil reservoirs. Moreover, it can favor AI experimentation, especially in terms of research and idea development – the more GPUs available, the more experiments can run simultaneously.
2. Big Data analysis and forecasting (finance, climate, retail)
Supercomputers can process enormous datasets for actionable insights, which can improve data-driven decision-making by way of forecasting. In the finance field, computational power can enhance AI models’ capability to simulate risks, optimize investment portfolios or anticipate market disruptions. For climate science, it can power models to predict extreme weather events and model climate change scenarios, which could then help branches of public service to prepare and protect populations and infrastructure accordingly. As for retail, models powered by supercomputers can optimize supply chains, forecast demand, and improve inventory management.
3. AI and machine learning (technology, e-commerce, automotive)
Supercomputing is essential to train large AI models that require significant resources, allowing them to perform autonomous tasks in a safe and reliable way. For example, finetuning LLMs, which contain massive amounts of data, or finetuning algorithms for the real-time identification of objects will push forward advancements in autonomous driving developments. In the field of e-commerce, predictive models that analyze customer behavior can provide more accurate personalized recommendations, while chatbots can provide customer interactions that feel even more seamless and natural.
A supercomputer for your AI models
Computational power is key to boost the efficiency and speed of AI models. For example, iGenius is building one of the biggest supercomputers in the world, Colosseum, to support the training, deployment, and fine-tuning of advanced models from the company’s AI product portfolio. This supercomputer will deliver peak performance for organizations that require maximum computational performance.
Built on NVIDIA’s state-of-the art Blackwell platform, Colosseum leverages thousands of Grace Black Superchips, specifically designed for real-time trillion-parameter inference and model training. It includes 115 exaflops of computational performance, which will accelerate AI workloads. This power is made possible by all the hardware involved: CPUs, memory, storage, but most importantly GPUs, which are of the latest generation and therefore more powerful than ever. The scale of the supercomputer is basically what makes it so powerful.
With Colosseum, organizations adopting AI models can benefit from:
1. Fully isolated AI
Colosseum ensures complete data privacy, even when multiple models are deployed on the same supercomputer, with no risk of breaches or data infringement.
2. Chip-to-frontend experience
Colosseum provides customers full control of the entire AI value chain, from data integration to end-user delivery, ensuring an efficient deployment experience.
3. Sovereign infrastructure
Colosseum supports sovereign deployment of AI models for highly regulated industries, such as heavy industry, finance and government, ensuring maximum precision, reliability, privacy, and safety.
Shaping the future on a global scale
As AI and Big Data continue to evolve, the demand for computational power will grow, particularly as companies scale by using increasingly larger, more advanced models that perform better.
Training larger models requires more computational resources. While this doesn’t imply that a supercomputer is essential for every model, using such a powerful infrastructure is the most effective way to maximize the ROI in a sizable model, by training it on the fastest and most advanced hardware available. For big data or complex tasks, such as weather forecasting at scale, the need for substantial computing power has always been present. Yet, the more data is processed, the greater the hardware requirements are.
Companies must align their computing infrastructure with their strategic goals. Evaluating which type of infrastructure best meets their needs is crucial to ensure long-term success and scalability.
Building supercomputers is both challenging and expensive, but the current demand for computing power is huge. The US is investing billions in bigger and more powerful data centers because AI models perform better with greater computational capacity (in other words, the more computing power available, the better they can be trained). In fact, the Trump Administration backed up a joint venture worth $500 billion – between tech giants OpenAI, Oracle, and SoftBank – to build an AI infrastructure on American soil.
Meanwhile, Europe’s contribution lies in the development of strong computing centers like Cineca and the Barcelona Supercomputing Center. Initiatives such as InvestAI – launched by Commission President Ursula von der Leyen to mobilize €200 billion – fosters hope that further investments will accelerate innovation and ensure the EU remains competitive on the global stage.
By building one of the world’s largest data centers, iGenius is actively contributing to the EU’s AI commitment. Furthermore, iGenius CEO Uljan Sharka is confident that investing in Europe will set off a domino effect, attracting more investors, and solidifying the region’s position as a major player on the world stage. “This is the only spark needed, and once ignited, there will be no limit to what Europe can achieve because the resources will follow,” he said. “The talent is here, and we have this open approach–we want to plant the seeds so that others can build on them.”
Frequently Asked Questions
Supercomputing refers to the use of extremely fast computers to perform complex and large-scale calculations while HPC involves the use of multiple computer clusters, working together to process these complex computations. In other words, supercomputing focuses on individual powerful machines while HPC leverages a collection of computing resources to achieve high performance.
AI and supercomputers work in synergy. Supercomputers provide computational power required to train and run complex AI models at scale, enabling advancements in deep learning, natural language processing and other AI features. Conversely, AI uses supercomputers by optimizing tasks like data analysis, simulation, and system efficiency. Together, they empower each other to deliver cutting-edge performance.
When comparing cloud computing to supercomputing, the key difference lies in the architecture and purposes. Supercomputers are typically housed in specialized data centers and operate over high-speed networks, focusing on executing massive parallel tasks with specialized hardware. However, cloud computing provides scalable and flexible computing resources over the Internet, allowing users to access and use computer power as needed without investing in specialized hardware. While supercomputing is dedicated to high-performance tasks, cloud computing offers versatility and accessibility to a wide range of applications.
