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Best CPUs for deep learning in 2023 – AMD, Intel, and budget picks

With ChatGPT and Bard on the rise in popularity, the best CPUs for deep learning now are at the forefront of the conversation
Last Updated on August 14, 2023
Top picks for CPUs for deep learning.

Choosing the right CPU (central processing unit) for your deep learning project can make all the difference in terms of efficiency, speed, and accuracy. In this article, we will explore the best CPUs for deep learning and discuss the key features to look for when making your decision.

The ever-expanding field of deep learning has been transforming various industries, including healthcare, finance, and autonomous vehicles. As a result, there is an increasing demand for high-performance computing to handle the intensive computational workloads necessary for training and inference in deep learning systems. At the heart of any deep learning system is a powerful CPU that can handle the intense computational workloads required for training and inference.

Whether you’re just starting out or are a seasoned data scientist, this guide will help you find the perfect CPU for your next deep-learning project.

Products at a Glance

How do we pick the best CPU for deep learning?

When it comes to selecting the best CPU for deep learning, there are several key factors to consider. First and foremost, the CPU’s core count and clock speed are critical in ensuring the fast and efficient processing of data.

Deep learning involves working with massive datasets, and having a high core count and clock speed can significantly reduce the time it takes to train models. Additionally, a large cache and high memory bandwidth are essential for minimizing data retrieval time and enabling fast data transfer between the CPU and memory.

Another critical consideration when selecting a CPU for deep learning is its compatibility with popular deep learning libraries such as TensorFlow, PyTorch, and Keras. These libraries are widely used in the data science community, and having a CPU that can effectively support them can streamline the development process.

Additionally, the CPU’s ability to support the complexity of vectorization is crucial in ensuring the efficient processing of large datasets. Low-latency interconnects are also vital for enabling efficient communication with other hardware components in the system, such as GPUs or RAM.

If you want to learn more about deep learning, then head over to our Machine learning vs Deep learning in AI guide. As well as, our Is the Intel Core i9-13900KS good for deep learning article?

Our Recommended

Best CPUs for deep learning in 2023 – AMD, Intel, and budget picks

  • Boost speed up to 6GHz
  • Leading multi-threading performance
  • Premium binned silicon
  • Overclockable
  • Pricey
  • Power consumption

The Intel Core i9-13900KS is widely regarded as one of the best CPUs for deep learning. Its processing power is so impressive that it can even rival AMD Threadripper CPUs, making it unnecessary to opt for one of those. One of the most significant advantages of the 13900KS is its 20 PCIe express lanes, which can increase even further with a Z690/Z790 motherboard. This is crucial since many deep learning tasks rely on the GPU, and the extra lanes provide more power for GPU acceleration.

Additionally, this processor boasts an impressive 24 cores, 32 threads, and 36MB of L3 cache. With the ability to boost up to 6GHz, this CPU delivers unparalleled performance for a consumer-grade processor. It is important to note that this is a relatively expensive chip, and it requires adequate cooling due to its power consumption.

Despite these factors, for serious professionals, the Intel Core i9-13900KS is undoubtedly a worthwhile investment. Its exceptional processing power, compatibility with deep learning libraries, and additional PCIe express lanes make it an ideal choice for deep learning tasks.

  • Compatible with PCI Express 5.0 for high-speed data transfer
  • Higher boost frequencies
  • Overclockable
  • Requires a compatible AM5 socket
  • Higher TDP (170W) may require more power and cooling

The AMD Ryzen 9 7950X is a powerful CPU that is well-suited for deep learning tasks. With its Zen 4 architecture and TSMC 5nm lithography, this processor delivers exceptional performance and efficiency. It boasts 16 cores and 32 threads, allowing for efficient parallel processing and enabling faster data processing for deep learning algorithms.

The Ryzen 9 7950X’s base frequency of 4.5 GHz and boost frequency of 5.7 GHz provide outstanding clock speeds for speedy computations, while the massive 64MB L3 cache provides ample storage for data manipulation. This processor’s TDP of 170W is relatively high, but this is to be expected from a high-performance processor that can handle demanding workloads.

Moreover, the Ryzen 9 7950X is compatible with the latest Socket AM5, ensuring that it will work with the latest motherboards. The launch MSRP of $699 is a reasonable price point for a high-end CPU that delivers the level of performance required for deep learning.

  • Hybrid technology performs great
  • Great performance
  • Overclockable
  • Great price
  • Low core count

For those on a budget or concerned about power consumption, the Intel Core i7-13700K is an excellent option for deep learning. Unlike other Intel CPUs, this chip delivers excellent performance and even outperforms Intel’s own 12th-Gen Core i9-12900K.

While there are cheaper options available, we do not recommend compromising on processing power by going for something like the Ryzen 5 7600X or the Core i5 13600K. These processors may save you money, but you’ll miss out on higher clock speeds and find yourself wanting more processing power.

The Core i7-13700K is a great value-for-money option that offers high clock speeds and is also easier to cool than the more powerful i9-13900K. It delivers excellent performance for both gaming and productivity and provides an affordable alternative to more expensive CPUs. If you’re looking for a budget-friendly option that can handle deep learning workloads, the Intel Core i7-13700K is an excellent choice.

  • The strongest single- and multi-thread performance
  • High boosted frequencies
  • Overclockable
  • DDR5 and PCIe 5.0
  • DDR5 is pricey
  • Maybe too much for the 3060 Ti in some cases
  • Needs an AM5 motherboard

The Ryzen 9 7900X is not only a great option for gaming and desktop applications but is also one of the best deep-learning CPUs. Its 12 cores and high clock speeds make it a great choice for handling large datasets and intense computational workloads. The Zen 4 architecture provides significant improvements in instruction per clock (IPC), which translates into faster processing times and more accurate results.

Furthermore, the Ryzen 9 7900X has been designed with deep learning in mind. It features a high-end core architecture that provides great performance for neural networks and other machine-learning tasks. This makes it a versatile option that can handle a wide range of applications beyond gaming and desktop productivity.

In conclusion, the Ryzen 9 7900X is a powerful and affordable CPU that offers excellent performance for deep learning tasks. With its combination of high clock speeds, multi-core architecture, and Zen 4 technology, it is a great option for those looking to build a high-performance deep learning system without breaking the bank.

Features and considerations

The first thing to consider is how demanding a PC you’ll be looking to build. For demanding professional workloads, it is advisable to opt for AMD Threadripper or Intel Xeon W CPUs. These CPUs are preferred due to their capability of offering more lanes to accommodate multiple GPUs, making them the ideal choice for serious professional workloads.

If those are not the cases and you don’t need a CPU with multiple GPU lanes, then you’ll need to choose a strong GPU with a high core count to enable parallel processing of large amounts of data. The high CPU cores is the more crucial aspect to consider. Once that’s taken care of, you’ll need a high-frequency CPU, so the unit can perform multiple calculations fast.

So far, these are the usual requirement for a high-end CPU. To be a bit more specific about what you’ll be looking for, consider low-latency interconnects. The CPU should enable efficient communication with other components in the system, such as graphics cards or memory.

Moreover, the CPU should support popular deep learning libraries such as TensorFlow, PyTorch, and Keras that have a focus on artificial and deep neural networks.

To make your PC setup complete, also consider investing in a GPU that comes with NVIDIA tensor cores. This is because tensor cores are specifically designed to perform matrix operations, which are a key part of deep learning algorithms.

If you are looking for more recommendations, then head over to our best laptops for programming article.


Do I need a good CPU for deep learning?

Yes, you definitely need a good CPU for deep learning. These AI models are computationally intensive, and they require a lot of processing power to train and run. Ensuring you invest in a high-quality CPU will help to speed up the training process and improve the performance of your models.

Our Verdict

In conclusion, there are several great options when it comes to choosing the best CPU for machine learning (ML). We have shown you some great offerings including both AMD and Intel processors.

The Intel Core i9-13900KS stands out as the best consumer-grade CPU for deep learning, offering 24 cores, 32 threads, and 20 PCIe express lanes. The AMD Ryzen 9 7950X is another great choice, with 16 cores, 32 threads, and a 64 MB L3 cache. For those on a budget, the Intel Core i7-13700K is an excellent option, delivering excellent performance and value for its price. Finally, the AMD Ryzen 9 7900X rounds out our list with impressive specifications and strong performance in both gaming and desktop applications.

When choosing the best processor for deep learning models, it’s important to consider factors such as core count, clock speed, cache size, and PCIe express lanes. These factors all play a role in determining the CPU’s ability to handle the intense computational workloads required for training and inference in machine learning models. By carefully considering these factors and the unique needs of your project, you can find the perfect CPU to help you unlock the full potential of your machine-learning workloads.