What Are the Best Processors for Machine Learning?
Out of the many options of processors for machine learning, choosing a CPU can be tricky, not because of the various options but because GPUs are usually the main focus.
We guarantee that you will need both a high-performance CPU AND GPU for effective machine learning processing. All good graphics cards and AI accelerators require a competent CPU to keep them fed. We will go over why you need a top-performing CPU and then give our recommendations for the best processors for building your next machine learning system.
Why Does a CPU Matter for Machine Learning?
Training and deploying machine learning models are predicated on collecting, analyzing, and interpreting large amounts of data to perform tasks ranging from simple automation and data analysis to generative AI and prediction models.
CPUs are essential in processing data quickly for memory transfer, storage, retrieval, and distribution to accelerators, drives, and the network. The faster your CPU can request, retrieve, store, and distributed data, the faster your machine/deep learning model can generate results.
Why You Need High-Performance CPU for Machine Learning
For all computers, you'll need to include a CPU regardless. But consider a high performance CPU for your system where CPU utilization leaves out the GPU.
There are times when your or a team is working with the datasets where data is constantly being changed, removed, and updated. This would be something similar to how a group Google Sheet or Microsoft Excel sheet works with algorithms inserted based on the changing data.
CPU will do everything you need in this specific instance is because storing and retrieving data in quick succession is not a GPU workload. CPUs excel at being able to quickly transfer data back and forth, when compared to a GPU.
IIn this specific yet common instance, GPU utilization is low. A CPUCPU can get the job done, and a high performance CPU can make quick work. The GPU exists to supplement more complex workloads that include math, convolutions, neural networks, and sorts. In simplest terms, a CPU can handle smaller generalized 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). Choosing a machine to perform optimally in both scenarios warrant choosing a good CPU and GPU.
The Best Processors for Machine Learning
Now that you understand the distinction between CPUs and GPUs in the context of machine learning, you can make informed decisions about which components to include in your machine learning, deep learning, or other AI-oriented computer build.
For both Machine Learning and Deep Learning, consider is the quantity of available cores. A higher core count translates to faster processing and improved data distribution across GPUs and networking components. Another new technology is the incorporation of a more cache. Applications that handle substantial amounts of data can benefits from an increased on-die cache capacity. This additional cache operates as a buffer between CPU and RAM and even the actual data storage, subsequently enhancing data access speeds.
Best Desktop CPU: AMD Ryzen Threadripper PRO
When it comes to the desktop/workstation, there is no better option right now than the AMD Ryzen Threadripper PRO. It is an absolute beast of a CPU. For slightly more complex data interpretation, the Threadripper PRO could still probably do the job without the help of a GPU at all. The high core count and fast cores deliver great performance; the PCIe expansion lets users' slot multiple add-in card and accelerators at peak PCIe speeds whether they are NVMe drives, multi-GPU configurations, or high-speed networking.
Check out our breakdown review of the AMD Ryzen Threadripper PRO and see for yourself how it stacks up.
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.
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.
- 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.
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 Specifications
Model/Specs | AMD Ryzen Threadripper PRO 5995WX |
|---|---|
Architecture | Zen 3 |
Socket | sWRX8 |
Cores | 64 |
Threads | 128 |
Max Boost Clock | 4.5GHz |
Base Clock | 2.7GHz |
Default TDP | 280W |
L3 Cache | 256MB |
Total PCIe Lanes | 128 PCIe 4.0 |
Memory Support/Type | 8 Channel DDR4 |
Best Server CPU: AMD EPYC 9684X
Though recently releases, there is no reason to doubt the performance of the new AMD EPYC processors featuring 3D V-Cache. The 3D stacking technology AMD incorporates on the AMD EPYC 9684X is a revolutionary, delivering an additional 1.1GBs of L3 cache. 1.1GB is not a typo making the AMD EPYC 9684X the most powerful server processor with the most L3 cache perfect for machine learning, deep learning, and other data intensive applications.
As stated before, additional cache on die can drastically increase performance for data heavy applications. And machine learning is predicated on data, so it only makes sense to deliver and process data the fastest when it is in close proximity to the CPU.
Check out our release review of AMD EPYC Genoa-X for more information.
Model/Specs | AMD EPYC 9684X |
|---|---|
Architecture | Zen 4 (with 3D V-Cache) |
Socket | SP5 |
Cores | 96 |
Threads | 192 |
Max Boost Clock | 3.7GHz |
Base Clock | 2.55GHz |
Default TDP | 400W |
L3 Cache | 1.1GB |
Total PCIe Lanes | 128 PCIe 5.0 |
Memory Support/Type | 12 Channel DDR5 |
Other Good Processors
The AMD Ryzen Threadripper PRO (and other "non-PRO" Threadripper processors) is definitely the go-to choice for AI workstations and the AMD EPYC 9004X (and non-X processors) likewise for servers, but there are plenty of other options out there, most notably Intel's line of CPUs including 4th Generation Intel Xeon Scalable and Xeon W.
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. We can recommend the best solution to optimize system performance based on your requirements.
You can also browse CPUs and Processors on the SabrePC website. For more information contact us directly.
Bonus Recommendation - Best GPU for Machine Learning
NVIDIA RTX 6000 Ada is currently a top tier choice GPU for machine learning. While NVIDIA’s flagship H100 for just about anything and everything AI and machine learning related, the cost and availability make it unattainable for enthusiasts. You can still learn about the NVIDIA H100 here.
With this in mind, the NVIDIA RTX 6000 Ada is the flagship GPU for professionals used for high performance computing, simulation, design, and deep learning. Its versatility in many workloads is more than enough for most of your machine learning needs. However, the consumer version and well renowned NVIDIA RTX 4090 can also get the job done.
The RTX 6000 features 18,000 Ada generation CUDA cores and 48GB of VRAM to handle the largest datasets spanning from text, all the way to images and video. The processing power of the GPU can demolish workloads without skipping a beat. In an AMD Threadripper PRO workstation, configuration can scale with multiple GPU for even faster throughput.
Do I Need a GPU for Machine Learning?
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.
GPUs have thousands of specialized cores to do math compared to the smaller number of generalized cores in CPUs. GPUs can handle intensive tasks like reading and writing huge amounts of data, running complex mathematical algorithms to interpret that data, and generating 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. But they shouldn’t be compared because both supplement each other and both are just as important as the other in building a high-performing machine learning system.
Curious About Some of Our Other Picks for Machine Learning Components?
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!