Deep learning mostly takes advantage of tons of smaller cores rather than only a few very fast ones. This means that your GPU does most of the work in deep learning, not the CPU. However, you want to have a well-optimized machine for deep learning either way.
Because your GPU will work only as fast as the CPU allows it, choosing the best CPU for deep learning is fundamental. Most people that use deep learning software have more than one graphics card, which means that you will need a powerful CPU to feed enough information to them and to have enough PCIe links. Having enough RAM is also important for deep learning.
So, how to choose the best CPU for deep learning? Luckily, we have prepared a great list to get you started. By reading this article you will learn how choosing the right CPU for deep learning matters and which is the best CPU for deep learning that you can buy today.
Best CPU for Deep Learning
- Best Choice Overall: AMD Ryzen 9 3900X.
- Runner-Up: Intel Core i9-9900K.
- Ultimate Deep Learning CPU: AMD Ryzen Threadripper 3990X.
- Best CPU for Deep Learning Under $200: AMD Ryzen 5 2600.
Things to Consider Before Choosing the CPU
Before making your choice, you need to figure out what your budget is. You also need to take into account that you might want to buy something more powerful so that you can add more graphics cards down the line.
While CPU frequency is very important, the instructions per cycle, or IPC, are even more important. Because AMD Ryzen CPUs have improved IPC over Intel CPUs, most AMD chips will do a much better job.
There is also the fact that Ryzen CPUs have significantly larger L3 cache, which plays a very important role in machine learning and how it affects the GPU performance.
If you only getting started with deep learning, you might not be able to afford the best CPU for deep learning. Nonetheless, you can still make do with something more budget-oriented and replace it with a more powerful CPU at a later point.
This is why choosing a good ATX motherboard with multiple x16 PCIe slots is important. Also, it is better if the PCIe slots are 4.0 rather than 3.0 because you are likely to swap out graphics cards than CPUs for quick and cheap upgrades sometime later.
If you are training smaller data models, then you can use your CPU by itself. Even a 4-core CPU can do the job, but it will not be very fast. Also, you need to pay attention to the CPU TDP and that you have an appropriate cooler.
You can squeeze some extra performance out of your CPU by overclocking, so it may play a role in your CPU choice. Some deep learning software will not take advantage of faster cores or having more of them.
Your CPU choice matters the most if you are doing deep learning in Python and use PyTorch and Tensorflow. They can utilize all your CPU cores through multithreading, so even a Ryzen Threadripper CPU can be pushed to 100% usage. Also, doing any sort of image augmentation will benefit from a more powerful CPU with faster cores.
If you plan to build a workstation with multiple powerful graphics cards, then something like a Ryzen Threadripper 3990X is a great choice because it has 88 PCIe 4.0 lanes. It is significantly more than the unreleased Ice Lake Xeon CPUs that will support up to 64 PCIe 4.0 lanes, so the choice is obvious here. And the Threadripper will most likely cost less without any sacrifices.
But what is the best CPU for deep learning that does not cost as much as a used car? Here comes the list.
Top 4 Best CPU for Deep Learning
|RANK||PRODUCT IMAGE||PRODUCT NAME||SCORE||PRICE|
|1||AMD Ryzen 9 3900X||9.9 ✓||Check Current Price|
|2||Intel Core i9-9900K||9.7 ✓||Check Current Price|
|3||AMD Ryzen Threadripper 3990X||9.6 ✓||Check Current Price|
|4||AMD Ryzen 5 2600||9.6 ✓||Check Current Price|
1- Best Choice Overall – AMD Ryzen 9 3900X
Here is a beast of a CPU that can do anything you want it to. It has 12 cores and 24 threads, which allow for outstanding deep learning performance. And the CPU comes with an excellent stock cooler – the Wraith Prism with RGB.
You can easily find it on sale for under $500 nowadays, which is a steal for such a powerful CPU. Not only is this the best CPU for deep learning, but it can also game at very high framerates if you are interested in that when you are not working.
The CPU supports 24 PCIe 4.0 lanes, which should be enough for most. Note that most graphics cards will only use around 8 lanes and PCIe 4.0 has double the bandwidth of 3.0. So you can comfortably use two graphics cards with this CPU if you want to.
The CPU is built on the 7 nm node and it boosts up to 4.6 GHz, which will be enough for any sort of machine learning. And the 64 MB of L3 cache plays a huge role in the CPU’s performance as well.
With AMD Ryzen, you also have the benefit that the CPU can utilize faster DDR4 RAM to squeeze extra performance out of it. The Infinity Fabric can be overclocked for even more performance.
In addition to everything mentioned, the Ryzen 9 3900X also has a TDP of 105 W, so cooling it won’t be very challenging and it won’t make a significant impact on your electricity bill. Perhaps the only minor drawback is the lack of an integrated GPU, but it won’t matter with deep learning anyway.
2- Runner-Up – Intel Core i9-9900K
The Intel Core i9-9900K is slightly older than the Core i9-10900K, but the price of the 10900K makes it very hard to recommend in a world where the Ryzen 9 3900X exists.
Both the 9900K and 10900K are great CPUs but they are outperformed or trade blows with the Ryzen 9 3900X in deep learning performance. The i9-9900K will do a good job at deep learning nonetheless, so it is far from a bad CPU.
It boasts 8 cores and 16 threads that boost up to 5.0 GHz, which gives the CPU great single-threaded performance. It only has 16 MB of L3 cache and it only supports PCIe 3.0, with a maximum of 16 lanes. And it is built on the 14 nm node. All this makes it obvious why an equivalent AMD Ryzen CPU will blow it out of the water in machine learning software.
At least it has a lower TDP of only 95 W and it is compatible with most LGA 1151 motherboards. Also, Intel CPUs can’t take advantage of faster RAM, so you can go with cheaper sticks. If your choice is limited to Intel, this is the one to get.
Note that the Core i9-9900K will cost more for roughly the same performance when you set it against an equivalent Ryzen CPU, so try to grab it when it is on sale.
3- Ultimate Deep Learning CPU – AMD Ryzen Threadripper 3990X
This CPU is one of the most powerful processors that exist. It costs around $3,600, so it is not for everyone. This CPU uses the TRX4 socket, so the motherboard choice is limited only to the ultimate high-end, which makes sense with such an expensive CPU.
And with a 4.3 GHz boost clock, 288 MB L3 cache, 64 cores, and 128 threads, it is hard to choose something else that deserves the title of “best CPU for deep learning”, provided that you can afford it.
It has support for 88 PCIe 4.0 lanes and quad-channel DDR4 RAM, which means that you can run three or four graphics cards in your workstation without any compromises whatsoever. Having multiple NVMe SSDs will be possible without any problems as well, which is to be expected. If you are a professional who needs to do visual effects or process tons of data, this is the best that you can get.
It is hard to recommend any Intel Xeon CPU over a Threadripper because of the differences in features and cost-to-performance ratio. If you want a cheaper and less powerful workstation CPU, the Threadripper 3960X is also an excellent choice with its 24 cores. And the 32-core Threadripper 3970X is also a great choice.
But nothing comes close to the 64-core beast that the Threadripper 3990X is. In addition to the high price, you will also need a powerful cooler for its 280 W TDP.
4- Best CPU for Deep Learning Under $200 – AMD Ryzen 5 2600
The availability of the Ryzen 5 3600 as well as the high price of the Ryzen 5 3600X only leave the Ryzen 5 2600 as the best budget choice for deep learning. The Ryzen 3 3300X is slightly cheaper but also suffers from a lack of stock right now.
This only leaves the two-and-a-half-year-old Ryzen 5 2600 as a decent budget choice for any sort of machine learning that requires multiple cores.
You certainly won’t be disappointed by the Ryzen 5 2600 because it has 6 cores and 12 threads that boost up to 3.9 GHz. Thankfully, the CPU is unlocked, so overclocking it should not be difficult. And the 65 W TDP means that you do not need very powerful cooling either. The stock Wraith cooler will do a decent job even if the chip is slightly overclocked.
The CPU only has support for PCIe 3.0 x16, but you don’t want to use more than one graphics card with this chip in the first place. You can also pair this CPU with faster RAM for free performance.
The CPU will be enough for basic deep learning, so you can use TensorFlow and other software without any issues. You can pair even very powerful graphics cards with this CPU, such as an RTX 2070, which is more than enough for a beginner.
Also, you will enjoy the benefit of the excellent AM4 socket. Make sure that you buy a motherboard that supports the latest Ryzen 7 and Ryzen 9 CPUs for a future upgrade down the line. All in all, this is the best CPU for deep learning at a low entry cost.
Choosing the right CPU for deep learning is important. While most deep learning software utilizes the GPU more than the CPU, you still need a powerful CPU that won’t be a bottleneck. You also want to get enough RAM and a good motherboard.
If you plan to build a computer with three or four powerful graphics cards, any AMD Ryzen Threadripper will be great, but it will cost a pretty penny.
If you are looking for a CPU just to get started, the Ryzen 5 2600 is a decent option. You will not get the best performance, and it is an ancient chip, but at least it has 6 cores and 12 threads. An Intel Core i7 or i9 or an AMD Ryzen 7 or 9 will be the best CPUs for deep learning, so aim for that.
Most AMD Ryzen CPUs offer a much better value and performance in deep learning software, but Intel CPUs have an advantage in inference training. Ultimately, the CPU choice does not matter as much as the GPU choice, so whatever works fine with your GPU will not be a major factor in the end.
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