6 Best CPU For Machine & Deep Learning – Expert Reviews

By now, you already know that the GPU does most of the heavy lifting, but this doesn’t mean that you don’t need a powerful CPU.

If you’re just starting out, you don’t have to get the most powerful CPU on the market. There are more budget-friendly options that will handle your workload before you need to upgrade.

If you are a seasoned deep learning expert, you probably have several graphics cards that will need a powerful CPU.

This is where this list of the best CPUs for deep learning will come in handy.


Position
First Place
Runner Up
Best Budget
Snapshot
AMD Ryzen 9 5950X 16-core, 32-Thread Unlocked Desktop Processor
Intel Core i9-10900F Desktop Processor 10 Cores up to 5.2 GHz Without Processor Graphics LGA 1200 (Intel 400 Series chipset) 65W
Intel Core i7-10700KF Desktop Processor 8 Cores up to 5.1 GHz Unlocked Without Processor Graphics LGA1200 (Intel 400 Series chipset) 125W
What You Need To Know About The CPU

Insane multi-core performance for machine and deep learning

Optimized to handle models.

Best CPU for Tensorflow, machine and deep learning.

Prime
-
Rating
Position
First Place
Snapshot
AMD Ryzen 9 5950X 16-core, 32-Thread Unlocked Desktop Processor
What You Need To Know About The CPU

Insane multi-core performance for machine and deep learning

Prime
Rating
Position
Runner Up
Snapshot
Intel Core i9-10900F Desktop Processor 10 Cores up to 5.2 GHz Without Processor Graphics LGA 1200 (Intel 400 Series chipset) 65W
What You Need To Know About The CPU

Optimized to handle models.

Prime
-
Rating
Position
Best Budget
Snapshot
Intel Core i7-10700KF Desktop Processor 8 Cores up to 5.1 GHz Unlocked Without Processor Graphics LGA1200 (Intel 400 Series chipset) 125W
What You Need To Know About The CPU

Best CPU for Tensorflow, machine and deep learning.

Prime
Rating

Last update on 2021-12-07 at 21:22 / Affiliate links / Images from Amazon Product Advertising API


Preview
Name
Rating
Operating Frequency, Number of Cores and threads
 
AMD Ryzen 9 5950X 16-core, 32-Thread Unlocked Desktop Processor

Up to 4.9 GHz GHz, 16 Cores and 32 Threads
Intel Core i9-10900F Desktop Processor 10 Cores up to 5.2 GHz Without Processor Graphics LGA 1200 (Intel 400 Series chipset) 65W

Up to 5.3 GHz, 10 Cores and 20 Threads
AMD Ryzen 9 3950X 16-Core, 32-Thread Unlocked Desktop Processor

Up to 4.7 GHz GHz, 16 Cores and 32 Threads
Intel Core i9-9900K Desktop Processor 8 Cores up to 5.0GHz Unlocked LGA1151 300 Series 95W (BX806849900K)

Up to 5.3 GHz, 10 Cores and 20 Threads
AMD Ryzen 7 5800X 8-core, 16-Thread Unlocked Desktop Processor

Up to 4.7 GHz GHz, 8 Cores and 16 Threads
Intel Core i7-10700KF Desktop Processor 8 Cores up to 5.1 GHz Unlocked Without Processor Graphics LGA1200 (Intel 400 Series chipset) 125W

Up to 5.1 GHz GHz, 8 Cores and 16 Threads

Top 6 Best CPUs For Machine & Deep Learning Reviewed


Best CPU For Machine Learning – AMD Ryzen 9

AMD Ryzen 9 5950X 16-core, 32-Thread Unlocked Desktop Processor

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Pros

  • Best multi-core performance
  • Overclockable
  • Power efficiency
  • Good price-to-performance ratio

Cons

  • Needs a powerful cooler

Operating Frequency: 3.4 GHz | Max Frequency: 4.9GHz | Number of Cores and Threads: 16 cores and 32 threads | L3 Cache: 32 MB | TDP (Thermal Design Power): 105W

First on the list is the AMD Ryzen 9 5950X. On paper, this CPU is a beast. It has 16 cores, 32 threads and is based on the Zen 3 architecture. It, however, doesn’t come with a cooler which takes a little from its value.


Do the amazing specs translate to better performance in real life? Yes, they do.

The AMD Ryzen 9 5950X uses the 7nm node combined with other improvements to latency to ensure that you get the most out of this CPU.

When it comes to power consumption, the 5950X draws less than the i9 9900K. Here is the kicker, the former has double the cores of the latter.

Despite its low power consumption, you’ll still need to pair it with a decent cooler.

Is it worth buying? Yes, if you have about $800 to spend on the CPU alone, this is for you.

Also, keep in mind that it doesn’t come with a cooler thus you’ll need to factor in the cost of a premium cooler.

If you’re looking for insane performance, this is it. The AMD Ryzen 9 5950X is 7% faster than the 5900X and 23% faster than the i9-10900K.


Best CPU For Deep Learning – Intel Core i9

Intel Core i9-10900F Desktop Processor 10 Cores up to 5.2 GHz Without Processor Graphics LGA 1200 (Intel 400 Series chipset) 65W

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  • Very fast processor
  • Can be overlocked
  • Great for content creation and gaming

Cons

  • Needs a powerful cooler

Operating Frequency: 3.7GHz | Max Frequency: 5.3GHz | Number of Cores and Threads: 10 cores and 20 threads | L3 Cache: 20MB | TDP (Thermal Design Power): 125W

After its release, the Intel i9 10900KF was seen as a gaming chipset. At the time, Intel claimed that it was the fastest gaming CPU in the world.

Since then several processors with better performance and speed have been released.

The i9 10900KF is similar to the i9 10900K in all ways except that it lacks an integrated GPU. Both chipsets are based on the 14nm lithography process, which has been around for more than 5 years.

This also means that it will have fewer cores compared to it’s AMD price counterpart. It has only 10 cores compared to the AMD 3900X which has 12 cores.

However, it’s an improvement as the i9-9900K only has 8 cores.

This was Intel’s attempt to dethrone AMD, and it did well except for a few areas. The chipset doesn’t have a cooler; thus, you’ll need to get one.

In addition, if you’re upgrading from your existing Intel CPU, you’ll need a new motherboard. Pair it with the Z490 motherboard as the Z390 motherboards are not supported.

Fortunately, Intel has made some improvements as the i9-10900KF can hit 5.3GHz on Turbo Boost speeds.

The only caveat is that these impressive speeds are limited to a single core. On all cores, it can reach 4.9GHz.

As mentioned earlier, you’ll need a cooler as the chipset tends to heat up. It’s not as hot as the Ryzen 3900X, but it will need a hardcore cooler.


Best AMD CPU For Machine Learning & Deep Learning For The Money – AMD Ryzen 9 3950X

AMD Ryzen 9 3950X 16-Core, 32-Thread Unlocked Desktop Processor

Where To Buy

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Pros

  • Excellent multi-core performance
  • Great value
  • Comes with a cooler
  • Runs cool

Cons

  • None

Operating Frequency: 3.5GHz | Max Frequency: 4.7GHz | Number of Cores and Threads: 16 cores and 32 threads  | L3 Cache: 64MB | TDP (Thermal Design Power): 105W

The Ryzen 9 3950X has the perfect combination of features you want in a deep learning CPU. It has a higher clock speed combined with 16 cores and 32 threads.

It’s a performance-oriented chipset as it’s 30 times faster than its predecessor.

The improved performance, additional cores and threads translate to a higher price. That’s about 50% more than you’d pay for a Ryzen 3900.

It doesn’t come with a cooler and judging by its performance; you’ll need one. Add about $100-$200 to the purchase price.

If you can overlook the price, you’ll realize that the 3950X is a beast. Its base clock speed is 3.5GHz, and with boost, it can reach up to 4.7GHz.

It’s based on the Zen 2 architecture and features the 7nm fabrication which allows AMD to retain the TDP at 105W.

And the best part is that you won’t need another motherboard if you have an existing Socket AM4 motherboard.

Is it worth buying? Well, the 3950X is expensive, but you have to consider what you’re getting. This is 16 cores and 32 threads which are on par with some of the latest CPU models.

If you’re getting this for heavy-multitasking like deep learning, rendering, streaming, etc., then it’s worth considering.

The processor is powerful enough to handle deep learning and leave sufficient headroom for other tasks. It’s a sizable investment, but it’s well worth it.


Best Intel CPU For Machine Learning & Deep Learning For The Money – Intel Core i9 9900K 

Intel Core i9-9900K Desktop Processor 8 Cores up to 5.0GHz Unlocked LGA1151 300 Series 95W (BX806849900K)

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Pros

  • Very high single core performance
  • Overclockable
  • Excellent for gaming

Cons

  • No hyper-threading
  • No stock cooler included

Operating Frequency: 3.6GHz | Max Frequency: 5.0GHz | Number of Cores and Threads: 8 cores and 16 threads | L3 Cache: 16MB | TDP (Thermal Design Power): 95W

The Intel i9 9900K was released in 2018, and it was the fastest CPU at the time.

It was the best CPU you could buy, but since then Intel has released upgrades to this and AMD has released models with double the cores and threads.

Speaking of cores and threads, the i9 9900K has 8 cores and 16 threads. This was a 33% increase in the number of cores, which resulted in a bump in performance.

Its boosted clock speed is 5GHz which at the time was the best clock speed seen on an Intel CPU.

During its release, Intel bragged about the i9 9900K overclocking capabilities. They even mentioned that some experts hit 7.6GHz. That’s impressive, but you have to consider whether the CPU overheats or not.

Unfortunately, the i9 9900K begins to get unstable the moment you surpass 5.0GHz.

The only way to get past 5.2GHz without your CPU overheating is to pair it with a liquid helium cooler. Are you willing to invest in a high-end cooler?

It’s been several years since the i9 9900K was released, is it worth buying?

Yes, it may not be the fastest, but it’s decently priced and holds its own against recently released CPUs.


Best Budget CPU For Machine & Deep Learning – AMD Ryzen 7

AMD Ryzen 7 5800X 8-core, 16-Thread Unlocked Desktop Processor

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Pros

  • Excellent multi-core performance
  • Great value
  • Runs cool

Cons

  • Doesn’t come with a cooler
  • Can’t be overclocked much

Operating Frequency: Up To 4.7 GHzMax Frequency: Up To 4.7 GHzNumber of Cores and Threads: 8 Cores and 16 ThreadsL3 Cache: 36MBTDP (Thermal Design Power): 105W

The AMD Ryzen 7 is a processor that gets almost everything right. Performance, price, etc. you name it.

This makes it the best value processor on the market because you don’t need to spend a lot in other to get powerful performance.

An octa-core CPU that is powerful, thanks to its high operating frequency. This provides it with excellent multi-core performance making it a suitable choice for machine and deep learning.

It’s also one of the few CPUs that runs cool. That doesn’t mean you shouldn’t get a powerful cooler.

Compared to similar processors, the AMD Ryzen 7 will run the coolest when the same cooler is used for the processors.

But is it worth mentioning in a list of the best CPUs for deep learning?

Yes, it’s not expensive, and you’re getting a 3-5% increase in single and multi-core performance compared to the base model.


Best CPU For TensorFlow – Intel Core i7

Intel Core i7-10700KF Desktop Processor 8 Cores up to 5.1 GHz Unlocked Without Processor Graphics LGA1200 (Intel 400 Series chipset) 125W

Where To Buy

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Pros

  • Good gaming performance
  • Very affordable

Cons

  • Locked ratio multiplier

Operating Frequency: 3.8GHz | Max Frequency: 5.1GHz | Number of Cores and Threads: 8 cores and 16 threads | L3 Cache: 16MB | TDP (Thermal Design Power): 125W

If you’re on the market for a budget-friendly deep learning CPU, this is it.  The i7 10700KF has 8 cores and 16 threads, which is what you’d expect in a budget-midrange processor.

Its base frequency is 3.80GHz while the Turbo boost on all cores is 5.1 GHz.

Unfortunately, it doesn’t feature Thermal Velocity Boost, which means you can’t push your processor past the Turbo Boost speeds.

As expected, the i7 10700KF is based on the 14nm++ architecture. By now you probably know that the 7nm platform is better, which is why AMD processors are more efficient.

Fortunately. Intel managed to improve clock speeds and increase the thread count.

This not only translates to better performance but also increased power consumption. To mitigate power consumption, Intel switched the LGA1151 socket for the LGA1200 socket.

This helped improve power efficiency, but it still lags behind AMD’s 3900X or the 3800X.

Should you buy it? It’s not the fastest, but you don’t need the fastest CPU for deep learning, as mentioned earlier. Therefore, if this processor can support your deep learning tasks, then go for it.


Does CPU Matter For Deep Learning?

Yes, CPU does matter. Some will say that GPU is the heart of deep learning and they’re not wrong, but this doesn’t mean that the CPU is irrelevant.

You can get away with buying a cheap CPU if you’re only doing deep learning.

However, if you’re doing reinforcement learning, you’ll need the best CPU for deep learning. If you’re more into data loading, choose a CPU with more threads.

Also, keep in mind that you want a CPU that can support the number of GPUs you intend to use. 

Additional factors you’ll want to consider is the cost, number of cores and socket type.

Is AMD or Intel Better For Machine Learning?

Intel CPUs are better optimized to handle machine and deep learning software.

AMD, on the other hand, have the advantage of multi-core performance at a lower price point.

If you had to absolutely choose. AMD CPUs are the better option because of their insane performance at a lower price point.

Pair that with an NVIDIA GPU and you have got yourself a solid machine and deep learning PC build.

Are Ryzen CPUs Good For Machine Learning?

Yes, Ryzen excels in some areas such as multithreaded performance which deep learning favors. It has more cores than Intel and is very stable.

How Much RAM Do I Need For Deep Learning?

By now, you know that more RAM translates to faster speeds. Have you ever tried to work on a 2GB RAM laptop?

Everything loads slowly, and you can see how it will struggle when handling deep learning.

A 4GB RAM PC would be better; however, the minimum requirements for deep learning are 8GB.

Keep in mind that 8GB is just a starting point, you’ll need 16GB RAM or 32GB RAM to perform most deep learning tasks.

Why is GPU Better Than CPU For Deep Learning?

As mentioned earlier, the GPU is the heart of a deep learning build. GPUs are better at computing deep learning algorithms, unlike CPUs.

GPUs have hundreds or even thousands of cores that can compute multiple parallel processes. This gives them an edge over CPUs as the latter takes up jobs sequentially.

You see, the most resource-intensive process of deep learning is training. It can consume valuable time, especially if you’re running it on a CPU.

As mentioned earlier, CPUs run tasks sequentially; thus, you can expect it to take forever.

Fortunately, with a powerful GPU, you can cut down on time as they can parallelize tasks, finish computations faster and perform targeted tasks.

With this in mind, invest in a GPU with the largest bandwidth that you can buy. Also, consider the VRAM requirements of the algorithms you intend to run.

Lastly, invest in a GPU with a sufficient number of cores as they determine the speed at which your GPU can process data.

Conclusion

Now that you know the kind of CPU you need for deep learning, it’s time to dust off your tools and begin building your PC.

The individual components are expensive, which might turn off some potential builders.

You also have to ensure that you pair compatible components.

For example, as you’ve read, some of the Intel processors need a new motherboard and if you pair the processors with the wrong motherboard that’s money wasted.

That said, do you know how to build a PC? If not, the motherboard you choose will most likely have a manual on how to assemble everything. If you’re stuck, there are tons of guides and tutorials online.

So, grab the best CPU for deep learning and let’s start building that PC.

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