For decades, innovation in computing has followed a clear storyline: faster chips, smarter architectures, smaller transistors. First came the Central Processing Unit (CPU) — the brain of the modern computer. Then the Graphics Processing Unit (GPU) — the muscle behind everything from video games to AI. But now, as enterprise AI explodes in scale and speed, we’re hitting a wall. And it’s not at the chip level. It’s in the infrastructure that connects them.
Let’s rewind.
CPU vs. GPU: A Timeline of Evolution
CPUs Came First: the “Brains” of the Computer
- Introduced in the 1970s (Intel 4004 in 1971)
- Designed for general-purpose tasks: running programs, managing operating systems, handling logic and control.
- Evolved gradually over decades: faster clock speeds, more cores, better instruction sets.
- Still essential in every computing system today: servers, laptops, phones, data centers.
GPUs Came Later: Born for Graphics, Evolved for AI
- Introduced in the late 1990s (NVIDIA coined the term “GPU” in 1999 with the GeForce 256)
- Initially created to render 3D graphics in video games: massively parallel, repetitive tasks
- Around the 2010s, researchers realized GPUs were ideal for training AI models due to their parallel processing power.
- Since then, they’ve become the backbone of AI, powering everything from ChatGPT to self-driving cars.
CPUs vs. GPUs — And Their Infrastructure Needs
| Feature | CPU | GPU |
|---|---|---|
| Developed in | 1970s | 1990s |
| Designed For | General-purpose computing | Graphics, now AI and parallel workloads |
| How It Works | Serial processing — a few tasks at a time | Parallel processing — thousands of tasks at once |
| Ideal For | OS, orchestration, web apps | AI training, real-time inference, simulations |
| Network Sensitivity | Moderate — can run on local data | High — depends on fast, constant data streams |
| Infrastructure Needs | General-purpose connectivity | High-bandwidth, low-latency, scalable fiber networks |
| Bottleneck Risk | Low | Very high if data can’t move fast enough |
The Problem: GPUs Are Starving for Bandwidth
CPUs scaled gradually — and networks generally kept up.
But GPUs scaled explosively over the last 5–10 years, fueled by the rise of AI. We’ve gone from delivering megabytes to a CPU… to needing to stream terabytes per second to GPU clusters spread across regions.
And while compute has advanced, network infrastructure hasn’t. We’re still dealing with legacy provisioning cycles, siloed connectivity, and rigid architectures. Without high-performance fiber, AI’s most powerful chips sit idle. Not because the model failed, but because the data never arrived.
The Bottom Line:
We’ve spent decades perfecting how we process data. Now, the challenge is how we move it.
Because in the age of AI, it’s not just about the smartest chip, it’s about the smartest, fastest, most scalable network behind it.