CPU vs. GPU vs. TPU for AI and Machine Learning
Understand the distinct roles of CPUs, GPUs, and TPUs in AI and machine learning. Learn how each processor’s architecture impacts performance, flexibility, and scalability to help you choose the right hardware for training and deploying large models.
If you’ve ever wondered why training a large language model can feel painfully slow, unless you’re using the right hardware, you’re not alone. The world of AI and machine learning is powered by three main types of hardware: CPUs, GPUs, and TPUs. Each has its own origin story, strengths, and quirks.
Central Processing Units (CPUs) are the classic all-rounders, born in the early days of computing to handle everything from spreadsheets to operating systems. Graphics Processing Units (GPUs) were originally specialized in rendering graphics, such as those used in video games and visual effects, but quickly found a second life in AI due to their parallel processing capabilities. Tensor Processing Units (TPUs), by contrast, are a more recent development, custom-designed by Google specifically to accelerate machine learning workloads, particularly those dominated by large-scale matrix computations.
In this lesson, we’ll break down how CPUs, GPUs, and TPUs work under the hood, and why to pick one over the others for specific tasks.
How CPUs, GPUs, and TPUs work internally
Let’s understand the different between a typical CPU, GPU, and TPU based on their internal architecture and working mechanism.
CPU
A multi-core CPU is built around a small number of powerful, independent cores, each designed to excel at control flow, decision-making, and rapid task switching. Each core contains its own execution units, registers, and a fast L1 cache, enabling it to efficiently process ...