TPU vs GPU: Pros and Cons

TPU vs GPU, which is better for you?

As artificial intelligence (AI) continues to increase in popularity, there is a lot of buzz around TPUs and GPUs. Our experience is from configuring our OpenStack and providing managed private cloud.

A lot of people compare TPU vs GPU, but the two are very different components.

In this article, we’ll tackle TPU vs GPU by covering what exactly TPUs and GPUs are, what they do, and the pros and cons of each.

• What is GPU?
  • How Do GPUs Work?
  • GPU Pros and Cons
• What is TPU?
  • How Do TPUs Work?
  • TPU Pros and Cons
• How to Set Up vGPUs With OpenStack Nova
• Wrapping Up: TPU vs GPU

What is GPU?

GPU stands for graphics processing unit.

GPUs were originally designed and used for 3D graphics to speed up things like video rendering, but over time, their parallel computing ability made them an extremely popular choice for use in AI.

How Do GPUs Work?

GPUs work via parallel computing, which is the ability to perform several tasks at once. It is also what makes them so valuable.

GPU parallel computing enables GPUs to break complex problems into thousands or millions of separate tasks and work them out all at once instead of one-by-one as a CPU is required to do.

GPU Pros and Cons

The parallel processing ability makes GPUs a versatile tool and great choice for a range of functions such as gaming, video editing, and cryptocurrency/blockchain mining.

It also makes them perfect for AI and machine learning, which is a form of data analysis that automates the construction of analytic models.

This is because the modern GPU typically contains between 2,500–5,000 arithmetic logic units (ALUs) in a single processor which enables it to potentially execute thousands of multiplications and additions simultaneously.

One caveat about GPUs is they are designed as a general purpose processor that has to support millions of different applications and software. So while a GPU can run multiple functions at once, in order to do so, it must access registers or shared memory to read and store the intermediate calculation results.

And since the GPU performs tons of parallel calculations on its thousands of ALUs, it also expends large amounts of energy in order to access memory, which in turn increases the footprint of the GPU.

GPU is currently the most popular processor architecture used in deep learning, but TPUs are quickly gaining popularity for good reason.

What is TPU?

TPU stands for tensor processing unit and is a designated architecture for deep learning or machine learning applications.

Invented by Google, TPUs are application-specific integrated circuits (ASIC) designed specifically to handle the computational demands of machine learning and accelerate AI calculations and algorithms.

Google began using TPUs internally in 2015, and in 2018 they made them publicly available to others.

When Google designed the TPU, they created a domain-specific architecture. What that means is that instead of designing a general purpose processor like a GPU or CPU, Google designed it as a matrix processor that was specialized for neural network work loads.

By designing the TPU as a matrix processor instead of a general purpose processor, Google solved the memory access problem that slows down GPUs and CPUs and requires them to use more processing power.

How Do TPUs Work?

Here’s how a TPU works:

• TPU loads the parameter from memory into the matrix of multipliers and adders.
• TPU loads the data from memory.
• As multiplications are executed, their results are passed on to the next multipliers while simultaneously taking summation at the same time.

The output from these steps will be whatever the summation of all the multiplication results is between the data and parameters.

No memory access at all is required throughout the entire process of these massive calculations and data passing.

TPU Pros and Cons

TPUs are extremely valuable and bring a lot to the table. Their only real downside is that they are more expensive than GPUs and CPUs.

Their list of pros highly outweighs their high price tag.

TPUs are a great choice for those who want to:

• Accelerate machine learning applications
• Scale applications quickly
• Cost effectively manage machine learning workloads
• Start with well-optimized, open source reference models

How To Set Up vGPUs With OpenStack Nova

With Jacob Hipps, OpenMetal’s Principal Engineer
Want to explore GPU possibilities even further by learning about virtual GPUs within OpenStack? Watch an enlightening session that delves deep into the world of vGPUs with OpenStack Nova.

As an open source cloud computing platform, OpenStack Nova serves as the bedrock for building and managing virtual machines (VMs) in the cloud. Its flexible and scalable VM provisioning, resource management, and access control capabilities make it an indispensable project of the OpenStack ecosystem for cloud infrastructure.

During this session at OpenInfra Summit 2023, Jacob delves into the hardware requirements necessary to create a robust vGPU infrastructure, from GPUs to CPUs, memory to storage.

By the end of this comprehensive session, you’ll have the skills and confidence to leverage the power of vGPUs within OpenStack Nova.

Need vGPUs? Need GPUs? Schedule a meeting with an OpenMetal representative to discuss your needs.

Wrapping Up: TPU vs GPU

In the battle of TPU vs GPU, it really comes down to what you need a GPU or TPU to do, and the budget you have available for your project.

When it comes to AI, deep learning, or machine learning, both GPUs and TPUs have a lot to offer.

GPUs have the ability to break complex problems into thousands or millions of separate tasks and work them out all at once, while TPUs were designed specifically for neural network loads and have the ability to work quicker than GPUs while also using fewer resources.

If you are comparing one to the other and debating which one you should use, let us find a custom solution tailor-made to fit your needs.