What Makes GPUs the Powerhouse of AI

— Issue #18 of The Artificial Newsletter

When we talk about AI tools like ChatGPT or Midjourney, we often praise the model—but rarely acknowledge the hardware behind the magic. The truth is: without GPUs, none of this would be possible.

So, what exactly is a GPU? Why is it essential for AI? And how is it different from the CPUs we’re all familiar with?

Let’s unpack it.

🧠 Why CPUs Fall Short in AI Workloads

A CPU (Central Processing Unit) is optimized for serial processing—handling a few tasks at high speed. This works well for browsing the web or running a spreadsheet. But AI models need to perform millions of calculations simultaneously, especially during training.

That’s where GPUs step in.

GPU (Graphics Processing Unit) is optimized for parallel processing, with thousands of smaller cores that can execute multiple operations at the same time. Originally designed for rendering graphics, their architecture happens to be perfect for the matrix operations in deep learning.

⚙️ What Makes GPUs So Efficient for AI?

At the core of every deep learning model is a math operation called matrix multiplication. Training an AI model involves tweaking millions (or billions) of parameters, often by:

  • Multiplying large matrices (weights and inputs)

  • Backpropagating errors

  • Adjusting values repeatedly

GPUs handle these operations at scale because:

  • They have high memory bandwidth

  • They allow parallel execution of tensor operations

  • They’re optimized for floating point arithmetic, which is the standard for neural network calculations

🛠️ Real-World Example: GPT Training

Training a large language model like GPT-4 can involve:

  • Thousands of GPUs

  • Weeks of runtime

  • Exabyte-scale datasets

Even smaller open-source models like LLaMA or Mistral require several high-end GPUs for fine-tuning.

However, for hobbyists or builders, platforms like RunPod or Google Colab Pro let you rent GPU compute affordably for tasks like:

  • Image generation (Stable Diffusion)

  • Running a chatbot locally

  • Fine-tuning a custom classifier

🔬 Want to Experiment Yourself?

Try one of these GPU cloud providers:

You can start by:

  • Running Stable Diffusion

  • Fine-tuning a HuggingFace model

  • Using Whisper to transcribe audio

It’s a great way to learn what happens “under the hood.”

🧠 TL;DR

  • CPUs are generalists, good for basic tasks

  • GPUs are specialists, designed for massive parallel computing

  • AI models depend on GPUs for both training and high-speed inference

  • Renting a GPU gives you hands-on access to serious compute power

Next newsletter, we’re getting practical again:
🛒 Build an AI Meal Planner That Generates Grocery Lists Based on Your Fridge Contents.