From ChatGPT’s unsettling human-like prose to mind-bogging artworks generated by the likes of Midjourney, artificial intelligence is everywhere now. According to recent estimates, about 1.8 million people use AI tools daily; a number that’s likely to grow exponentially. However, since this technology is the spearhead of computing sciences, most people still don’t know how it works, let alone the role of GPUs (graphics processing units) servers in it.
Digital Team Effort
The efficiency of AI models depends on their ability to run multiple calculations simultaneously and process massive datasets in a split second. Initially, such tasks were handled by CPUs (central processing units). CPUs can manage a handful of cores designed for high-speed sequential processing.
However, the development of complex animated graphics (like those found in AAA games) has created a load of data that’s just too big for CPUs to handle alone. So, researchers developed GPUs that could manage this workload in parallel with CPUs. Unlike a regular CPU, GPUs have thousands of processing cores. It makes them useful for a range of data-intensive tasks, such as AI training.
The concept isn’t new to gamers, as the GPU is a vital part of any gaming device, whether a computer, console, or smartphone. Meanwhile, GPU servers are computers with multiple cores working along with a central processing unit. This combination creates a powerful setup capable of performing billions of matrix multiplications in a fraction of the time that a regular computer would.
Servers in Action
Due to ever-larger datasets, the number of data-intensive tasks has significantly increased. Such datasets are essential for essential tools to do their thing, from suggesting a new movie to generating content on the go. However, not many tasks are as demanding as training language learning models (LLMs).
LLMs involve text databases with billions (sometimes, trillions) of parameters. It requires intensive matrix multiplications at ultra-high speeds to do in seconds what a traditional computer would take years to achieve. Modern versions of this technology include tensor cores, specialized components designed for calculations of mixed precisions.
They are also behind the artificial intelligence models used in the medical and pharmaceutical industries. Thanks to hardware designed initially to create cheeky graphics, there are AI models capable of simulating new drugs and analyzing medical images, for instance. Likewise, it powers facial recognition models used in security systems.
GPU servers are also vital for climate scientists, who need to analyze immensely complex environmental conditions. More importantly, it enables the creation of much higher-resolution climate prediction models.
The Brains Behind AI
The structure of GPU servers provides the neurons that CPU-based AI lacked. They are the silent enablers of an increasingly complex system, where a simple yes-or-no answer involves billions of calculations.
Indeed, it represents a leap forward for AI-related sciences, becoming a game-changing assistant in key industries such as health, education, and security. The ongoing revolution in AI isn’t only about better algorithms, but also about cutting-edge hardware. Much more than a piece in this puzzle, GPUs are the very bedrock upon which modern AI is built.
