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How Different is a TPU from GPU?
by Daniel Smith Cloud Consultant | Content WriterWith the ever-changing landscape of processor options for empowering business applications, most business owners and tech enthusiasts are still trying to find what suits them the best! With GPUs in the picture and TPUs also on the table, it can be perplexing to pick one processing option that can make things easy. Moreover, it can be daunting to understand the difference between these two types of processors if you don't have an idea about either of them, especially when choosing among them for your specific business requirements.
Through this blog, we will delve into both types of processors and learn the differences between the two. Without further ado, let's get started.
In a nutshell, a TPU is designed for processing machine learning models, while a GPU is designed for rendering graphics. A TPU is composed of a matrix of tightly interconnected processing cores, while a GPU has a higher number of less tightly connected cores. It gives cloud GPU servers more flexibility to process data in parallel, but it can also lead to communication bottlenecks.
What is a TPU?
A TPU is a popular acronym for a Tensor Processing Unit. It is a type of processing unit that has eight cores. In other words, each TPU comprises eight cores optimized for 128x128 matric multiplies.
Simply put, TPUs offer a fast-processing performance, and one TPU is as quick as almost 5 V100 GPUs! Several TPUs together is hosted on a device called a TPU pod. For instance, TPU V3 Pod comprises 2048 TPU cores, and you can even ask for an entire pod from some TPU providers or a "slice" that offers a subset of 2048 cores.
What is a GPU?
To speed up the creation of images in a frame buffer intended for output to a display device, a specialized electronic circuit known as a Graphics Processing Unit is made to manipulate and alter memory. Cloud GPU servers like NVIDIA A2 GPU are popularly utilized in game consoles, embedded systems, mobile phones, personal computers, and workstations.
Where to Use a TPU?
- Models dominated by custom TensorFlow operations written in C++
- Huge models having enormous effective batch sizes
Where to Use a GPU?
- Particularly in retail and e-commerce, cloud GPU servers can simplify the automation of data enhancement.
- Data profiling, dependency and inference analysis, and data anonymization are all additional machine learning applications for GPUs such as NVIDIA A2 GPU.
TPU Vs. GPU -- The Main Difference Between the Two
GPUs are built on microarchitectures initially designed for 3D gaming and video rendering. These microarchitectures include large numbers of cores (up to several thousand) that one can use to execute many parallel instructions simultaneously. This capability is vital for deep learning because training large neural networks often requires the execution of many different operations in parallel.
On the other hand, a TPU is a processor that is designed specifically for machine learning - unlike GPUs, which is a processor that can be used for general-purpose computing but is particularly well-suited for graphics processing. Moreover, TPUs are based on a different type of architecture—known as an ASIC (Application-Specific Integrated Circuit).
All in all, businesses must have the right combination of these two powerful forces in the right balance per the organizations' unique needs. Consult with an experienced cloud provider like Ace Cloud Hosting to understand which technology your business needs more!
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About Daniel Smith 
Cloud Consultant | Content Writer
0 connections, 0 recommendations, 27 honor points.Joined APSense since, August 3rd, 2022, From Bethel Park, PA, United States.
Created on Jan 16th 2023 03:40. Viewed 113 times.
