When your Laptop Just Can’t Keep Up

We all have had times when during late nights of work and study, with many reports to finish and assignments to complete, our computers started glitching or even turned themselves off. As an empath, I like to think that my beautiful and expensive laptop can’t help but feel the stress I’m under and sacrifices itself to take the burden off me. But, oh well, as beautiful and heroic as that sounds, it’s not the case.

So, if we must blame someone for our misfortunes, it would be physicists who, to this day, for some reason, have not discovered the perpetual motion machine. Because of them, we still require external energy sources for all our machines to work properly. Now, as an empath again, I suggest we cut them some slack. However, for all my friends, especially those in scientific fields, who try to be efficient workers but are held back by their slow computers (bear in mind, this article is not encouraging you to blame your tools), I have a solution ready for you!

The Supercomputers You Didn’t Know You Could Use

Every child knows about Superman – a man with superpowers, the strongest and second-most-handsome of all men (after Christian Bale’s Batman, of course). What if I told you there are powerful machines out there in the world that surpass your laptop’s capacity by 330 thousand times and are, unironically, called supercomputers or HPC? Imagine a cluster of high-end computers (processors) working together in parallel, splitting chunks of data into smaller parts to process them much faster than a single computer ever could. That is what makes supercomputers so efficient.

Fun fact: the first supercomputer, CDC 6600, introduced by Seymour Cray in 1964, wouldn’t even compare to today’s smartphones. Think of how far we’ve come and how much more we can achieve with the growing market for HPCs.

How Supercomputers Work

Supercomputers work by breaking down complex problems into smaller tasks and distributing them across multiple processing units. CPUs (Central Processing Units) handle general-purpose tasks that require sequential processing, while GPUs (Graphics Processing Units) specialize in parallel processing, making them ideal for large-scale computations like AI model training or climate simulations.

Once data is uploaded to an HPC system, it is distributed across multiple nodes, where CPUs and GPUs perform calculations simultaneously, exchanging results in real-time. The processed results are then compiled, stored and accessed through specialized software, allowing researchers to extract insights, visualize data and collaborate globally over secure networks.

Since they’re designed to speed up the processing of massive amounts of data – and data is being generated and stored almost everywhere today – HPCs are incredibly useful in diverse fields, including, but not limited to, AI, health, cybersecurity, energy, defense, the automotive industry, climate studies, media and even the financial sector.

HPCs Are Powerful – But Also Expensive

HPCs are the best computers of our era, but, oh boy, are they expensive! The price of an on-premises HPC can range from $3 million to $1.2 billion, and they also require annual operating costs, including 10% of CapEx for energy. Among the TOP500 HPCs in the world, the US ranks #1, with 173 listed, and its El Capitan supercomputer, which claimed first place in November 2024, costs approximately $600 million.

Before you decide that HPCs have nothing to do with you, what if I told you that you don’t have to spend that much money? In the era of the internet and cloud computing, anyone in need of high computing power can access it without leaving their house.

Cloud Computing: Supercomputing Without the Super Costs

Cloud computing, put simply, is a technology that allows users to access and utilize computing resources like storage, servers and software over the internet on demand, without owning physical infrastructure. You don’t need to buy or build an entire HPC and wait for it to work properly – you can simply go online and get as much computational power as you need, scaling up or down when necessary.

HPCs are already provided by global cloud services providers like Oracle, Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and Alibaba Cloud, offering scalable, diverse and comparatively affordable solutions for complex computational tasks. The good news is that you don’t even need to spend money to try it out, as many cloud service providers offer free trials with access to HPCs included.

How Much HPC Power Do You Actually Need?

That said, the hardest part of using HPC is not the technology – it is figuring out how much computing power you actually need. Throwing more GPUs and CPUs at a problem won’t magically make everything faster – sometimes it just wastes resources. Research from Princeton University shows that carelessly increasing the number of processors can actually lead to diminishing returns, reducing efficiency instead of improving performance. That’s why scaling analyses are essential to finding the right balance.

A study by Cast AI, which analyzed 4,000 clusters running on AWS, Google Cloud and Microsoft Azure, found that, on average, companies use only 13% of their provisioned CPUs and 20% of their allocated memory – meaning they’re paying for far more than they actually use. Fortunately, cloud providers offer monitoring tools to help track usage, as well as pay-as-you-go pricing models that ensure you’re only charged for the computing power you actually need. If you’re feeling overwhelmed by all of this, many providers offer free training programs to help users optimize their resources and make informed decisions.

Try It Yourself: Experimenting With HPC Is Easier Than You Think

This is where real-world optimizations show just how much of a difference the right resource allocation can make. Take AI model training, for example – recent advancements have slashed GPT-2 training times from 45 minutes to just 3 minutes using 8×H100 GPUs, which cost only $3 per hour on the cloud. The best way to learn? Experiment! You can start for free on Google Colab, where you can access different GPUs and test your computing needs in a Jupyter Notebook environment – no setup required. GPU availability depends on demand, but once you hit the limits of the free tier, scaling up is surprisingly affordable. By testing and optimizing your workload, you will quickly realize that HPC isn’t just powerful – it can also be cost-efficient when used wisely.

Even if you have highly sensitive information that must remain secure (though I doubt we, ordinary people, have such thrilling secrets), private cloud services (cloud environments used exclusively by one organization) could be the answer. For governments, however, many still see the need to build their own HPCs to meet security regulations and achieve long-term cost efficiency. For instance, the El Capitan supercomputer was built specifically to secure the US nuclear stockpile and handle other national security-related classified tasks.

Final Thoughts

We may still have many years ahead before discovering the perpetual motion machine (you could call me a dreamer), but we certainly don’t need to wait for our governments to build on-premises HPCs to try them out. Who knows? Maybe your organization is more open-minded to new ideas than you think (you can definitely call me a dreamer now) and might even appreciate you introducing them to cloud-based supercomputers – they might even start incorporating them.