Introduction
For decades, central processing units (CPUs) were the undisputed kings of computing. They powered everything from early AI experiments to everyday word processors. But somewhere between video game realism and neural network revolutions, a new contender quietly rose through the ranks: the graphics processing unit, or GPU.
Today, GPUs are the backbone of deep learning, and understanding how we got here isn’t just a lesson in hardware evolution. It’s a story about parallelism, overlooked potential, and how a chip built for shooting zombies on-screen ended up driving breakthroughs in cancer research, protein folding, and natural language understanding.
Origins: Built for Games, Not Gradient Descent
The original purpose of GPUs was simple: make video games look good. Unlike CPUs, which are designed for flexibility and sequential tasks, GPUs excel at doing many calculations at once. Rendering a 3D world requires applying the same transformation matrix to millions of pixels simultaneously, perfect for parallel computation.
This specialization made them ideal for graphics, but for a long time, no one thought to apply them outside of that domain. In fact, as late as the early 2000s, machine learning researchers still leaned heavily on CPUs, which were easier to program and more familiar.
The Paradigm Shift: GPUs Meet Neural Nets
The real turning point came around 2012 with the now-legendary ImageNet moment. A team from the University of Toronto trained a deep convolutional neural network, AlexNet, using NVIDIA GPUs, and cut the error rate in half on one of the most important image recognition benchmarks.
Why did GPUs work so well?
- Deep learning relies on matrix multiplication, especially in training where backpropagation is key.
- GPUs, with thousands of smaller cores, are optimized to handle this type of repetitive linear algebra better than CPUs.
- Frameworks like CUDA (NVIDIA) and later TensorFlow and PyTorch made it easier to harness this power.
By 2015, almost every serious deep learning project was GPU-accelerated. Once GPUs proved their value in computer vision, researchers in other domains quickly took notice, especially in the life sciences, where data complexity rivals anything in image analysis.
That same architecture, optimized for image recognition, is now repurposed in cancer cell classification, cryo-EM data reconstruction, and protein folding models like AlphaFold.
CPUs vs GPUs: Not a Death Match, But a Specialization
Let’s be clear, CPUs are not obsolete. They’re still crucial for:
- Data preprocessing and serial tasks
- Managing I/O and system-level operations
- Running lightweight inference models on edge devices
But for training large neural networks, GPUs dominate. According to IBM and NVIDIA, modern training runs can be 10–100x faster on GPUs, especially when scaled across multiple cards.
And now, we’re seeing even more specialized accelerators enter the fray:
- TPUs (Tensor Processing Units) from Google
- FPGAs and AI ASICs from various hardware startups
- And yes, GPUs on the cloud, democratizing access for researchers and startups
A Philosophical Note: Why GPUs Matter Beyond Speed
Here’s where things get interesting. The rise of GPUs didn’t just change performance metrics, it unlocked new kinds of thinking.
Because training that once took months could now happen in days, researchers became more ambitious. They moved from shallow classifiers to massive language models. They iterated faster, experimented more, and pushed the boundaries of what AI could do.
It’s no coincidence that the same chipmaker dominating the GPU space, NVIDIA, is now at the heart of everything from generative AI to robotics. Jensen Huang’s vision, as profiled in The Atlantic and The New Yorker, wasn’t just faster chips. It was making AI actually possible at scale.
The Road Ahead: Still Evolving
Will GPUs reign forever? Unlikely. But for now, they are the go-to tools for training cutting-edge models. The next frontier may involve:
- Neuromorphic computing
- Photonic processors
- Or even quantum accelerators
But no matter what’s next, GPUs taught us a vital lesson: sometimes, the future of intelligence isn’t born in a lab, it’s hiding in your gaming rig.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
Bridge Informatics stays ahead of the curve on the latest AI and hardware innovations transforming computational biology. From selecting the right GPU infrastructure to optimizing neural network workflows, our team understands how to scale modern machine learning approaches for biological data. Whether you’re building deep learning models for cell classification or integrating AI into multi-omics pipelines, we provide the engineering and domain expertise to accelerate your work.
Click here to schedule a free introductory call with a member of our team.