Best Processors for Machine Learning

What Are the Best Processors for Machine Learning?

Choosing one out of the many options of processors for machine learning can be tricky. Not because of the crazy amount of options available, but because there is always the debate between whether or not you should rely on a CPU for machine learning or a GPU.

You need both for effective machine learning processing. All good graphics cards require a good CPU to help run them.

Computer build with processors for machine learning, Image Source

In this article, we will go over why you need a CPU and then give our recommendations for the best machine learning processors.

CPU for machine learning, Image Source

When considering the best processors for machine learning we must consider the viability of CPUs. A common question is whether or not a CPU is even important in machine learning. Do you need a CPU for deep learning?

Machine learning can be used to collect, analyze, and interpret large amounts of data and use it to perform a task. Some of these tasks can be simple automations or more complex algorithms generating various types of content with the data.

CPUs can be beneficial in these processes as quicker responses for memory transfer and quickly storing and retrieving data. This capability is important in every build to shave off time for your machine learning and deep learning programs. The faster data can be requested, retrieved, and stored, the faster you can get your results.

The question here is whether or not a high-quality CPU (without the addition of a GPU) is, more or less, the best choice for your machine learning needs. The short answer: no, except in specific circumstances. The slightly-longer answer: there are niche-specific use-cases where a CPU alone would be perfect for what you would need for a machine learning program, making CPUs the best processors for machine learning in those instances.

In general, you need to include a CPU as a key component of your system for machine learning. However, there are use cases where you may only need a CPU — leaving out a GPU.

Image Source

These are going to be times when you are working with small datasets or where data is constantly being changed, removed, updated — again, all in small chunks of data. This might be for something where groups of people are inputting data onto a shared document and your machine learning program is simply reading this new data, running some brief programming, and creating basic ways of conveying that information to all users.

This would be something similar to how a group Google Sheet or Microsoft Excel sheet works with algorithms inserted based on the changing data.

The reason why a CPU will do everything you need in this specific instance is because you are going to be storing and retrieving data fairly quickly, minimally processing or interpreting data, and spitting out results constantly. CPUs excel at being able to quickly transfer data back and forth, when compared to a GPU.

In this one specific instance, you could forego a GPU for machine learning entirely and focus on a single quality CPU. Outside of this specific instance, you will pretty much need a GPU in addition to a CPU. In simplest terms, a CPU can handle smaller tasks quickly but is limited to what it can process concurrently (at the same time). A GPU can handle larger batches of data by splitting it up and processing in parallel (concurrently).

Now that you know the difference between CPUs and GPUs for machine learning you can make an informed decision about what parts to buy for your machine learning, deep learning, or other AI computer build.

Machine learning processors, Image Source

To keep things simple, the most basic features to look for are the number of cores available. You want to have as many cores available as possible. There are other factors, but this is probably one of the most important factors as we choose the best processors for machine learning on the market.

Best CPU: AMD Ryzen Threadripper PRO

When it comes to the best CPU, there is no better option right now than the AMD Ryzen Threadripper PRO. It is the absolute best CPU on the market and, even for slightly more complex data interpretation, the Threadripper PRO could still probably do the job without the help of a GPU at all.

Check out our breakdown review of the AMD Ryzen Threadripper PRO and see for yourself how it stacks up.

AMD Threadripper Pro Shell and Insides, Image Source

The nice thing about using this as a CPU for machine learning is that you won’t need to replace it any time soon. It is far beyond mid-range and even some higher-range CPUs in terms of raw computing power. With 64 cores it maxes out what is currently available on the vast majority of CPUs and will probably stay near the top of the market as the best CPU available for the foreseeable future.

Start with an amazing CPU and it makes the search for a complementary GPU that much simpler.

AMD Ryzen Threadripper PRO features:

  • 7nm process technology, delivering an unmatched CPU core density for professional workloads.
  • Support for 128 PCIe 4.0 lanes, enabling a variety of advanced configurations leveraging next-gen GPUs and storage devices.
  • Only professional workstation processor to support PCIe 4.0, delivering twice the I/O performance over PCIe 3.0.
  • Up to 64 cores of processing power.
  • AMD Secure Processor, which is a powerful, integrated, dedicated security processor designed to establish a hardware root-of-trust to help secure the processing and storage of sensitive data and trusted applications.

AMD Ryzen Threadripper PRO specs:

  • Platform: Desktop
  • Product Family: AMD Ryzen™ PRO Processors
  • Product Line: AMD Ryzen™ Threadripper™ PRO Processors
  • # of CPU Cores: 64
  • # of Threads: 128
  • Max. Boost Clock: Up to 4.2GHz
  • Base Clock: 2.7GHz
  • Total L1 Cache: 4MB
  • Total L2 Cache: 32MB
  • Total L3 Cache: 256MB
  • Default TDP: 280W
  • Processor Technology for CPU Cores: TSMC 7nm FinFET
  • Unlocked for Overclocking : No
  • CPU Socket: sWRX8
  • Socket Count: 1P
  • Max. Operating Temperature (Tjmax): 90°C
  • PCI Express® Version: PCIe 4.0
  • System Memory Type: DDR4
  • Memory Channels: 8
  • System Memory Specification: Up to 3200MHz

Other Good Processors

The AMD Ryzen Threadripper PRO (and other “non-PRO” Threadripper processors) are definitely becoming the go-to choice for AI workstations and servers, but there are plenty of other options out there, most notably Intel’s line of CPUs.

If you are unsure which processor to get based on the other components in your system, the best bet is to discuss what you want to do with a SabrePC engineer. They can recommend the best solution to optimize system performance based on your requirements.

You can also browse CPUs and Processors on the SabrePC website here, which will also tell you what’s in stock and pricing:

CPUs and Processors

Best GPU: NVIDIA TITAN XP

While we would normally recommend the NVIDIA GeForce RTX 3090 for just about anything and everything, machine learning is unique in that it may actually be better and more efficient to have multiple good GPUs for your machine and deep learning algorithms.

With this in mind, the nearly 4,000 cores of the NVIDIA TITAN XP can be more than enough for most of your machine learning needs.

NVIDIA TITAN XP, Image Source

Adding an additional TITAN XP can get you almost all the way to as many cores as the RTX 3090 at less than half the price. For that reason alone, we highly recommend the TITAN XP as an ideal alternative GPU for machine learning, especially if you are looking for something on a budget.

In nearly every example of machine learning you are going to want to use a GPU (or a few) alongside your CPU. The CPU is still important, but, depending on the project or application, the GPU will be the powerhouse of the machine learning programs you will be using.

The reason why using a GPU for machine learning is so important is because GPUs are far faster at hardware intensive tasks. They are designed to handle high-end graphical data and create something visual out of data points. The highest quality GPUs have thousands of cores to do this compared to the smaller number of cores typically available in CPUs.

GPUs can handle intensive tasks like reading and writing huge amounts of data, running complex mathematical algorithms to interpret that data, and generate complicated outcomes with the interpreted data. A CPU will struggle to do all of this in a fraction of the time a decent GPU could.

In the debate of a CPU for machine learning versus a GPU, the GPU wins 99.9% of the time.

Let us know what you think! Are these great picks of processors for machine learning or do you have another idea? If you are wanting to build your own AI computer, then we would love to help you build a custom system to your requirements. Feel free to contact us and we can help get you pointed in the right direction today!

In the meantime, you can find other articles and comparisons elsewhere on the SabrePC blog. Keep a lookout for more helpful articles that will be on the way soon!

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James Montantes

James Montantes

Interested in HMI, AI, and decentralized systems and applications. I like to tinker with GPU systems for deep learning. Currently at Exxact Corporation.