Apparently more and more companies are realizing that if they want to get the best performance at the lowest price, they’ll need to make their own computer chips. That's exactly what’s driving the development of Google’s TPU or Tensor Processing Units.
What Is a Google TPU?
You’ve heard of CPUs, and you’ve heard of GPUs, but what the heck is a TPU? It’s a specialized microchip that’s designed to process tensor calculations. The sort of math that drives modern neural-net AI. You might recall that modern GPUs from NVIDIA have tensor cores, which is a specialized area of the GPU that makes AI features like DLSS possible. Well, a TPU is a chip that’s dedicating virtually all its silicon real estate to that job.
Since almost the whole chip is dedicated to massive parallel matrix manipulation, it can run AI-related workloads with high performance and, perhaps most importantly, high energy efficiency. Rather than optimizing for gaming, visualization, or mixed compute workloads, TPUs are tuned almost exclusively for training and inference of neural networks.
Importantly, TPUs are not sold as standalone hardware. They are deployed inside Google’s data centers and made available through Google Cloud as rentable compute instances.
Image Credit: Google
How TPUs Differ From CPUs and GPUs
CPUs are general purpose processors with lots of diversity in their processing elements. They can switch tasks rapidly, which is why even a single-core CPU can simulate multitasking.
GPUs excel at parallel workloads and remain the dominant accelerator for many AI tasks. TPUs take a different approach: they trade flexibility for efficiency.
TPUs use large systolic arrays to stream data through matrix units, which minimizes memory bottlenecks and maximizes utilization. This design is highly effective for deep-learning architectures, like large transformer models, that involve repetitive mathematical operations.
TPU Generations and Performance Scaling
Google has rapidly evolved its TPU design. Initial versions focused on inference, while newer generations support large-scale training. Recent TPUs enhance compute, memory, and interconnect bandwidth, enabling thousands of chips to function as a single system.
Crucially, TPU "pods" network accelerators with high-speed interconnects, allowing models to scale efficiently across the entire pod. This pod-level architecture is vital for training extremely large models.
Where TPUs Are Used Today
TPUs, which power Google services like Search and Translation, are externally available via Google Cloud for:
- Training large language models
- Cost-efficient, scaled AI inference
- Academic and scientific ML research
They offer mature support for TensorFlow and JAX, with increasing compatibility for PyTorch. So while you can’t buy your own TPU hardware from Google, you can access it by effectively renting the hardware in the cloud.
What This Means for Businesses
TPUs are a strategic cloud option, not an on-premise replacement, best for large, consistent, highly parallel workloads where training cost or power efficiency is key. While GPUs are the best all-around accelerators, TPUs offer cost and performance advantages with the right models and software.
