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Best Workstations for Deep Learning, Data Science, and Machine Learning (ML) for 2021
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Best Workstations for Deep Learning, Data Science, and Machine Learning (ML) for 2022

Towards AI Team
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November 21, 2021
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Last Updated on January 7, 2022 by Editorial Team

Author(s): Towards AI Team

 

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

Best deep learning workstations, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Image by the Author.

It’s time to unleash AI supercomputing power right from your PC. We have looked at over 5000 desktops [1] and picked what we consider to be the best workstations for deep learning, machine learning (ML), and data science for every budget.

Helping Scale AI & Technology Startups to Enterprises | Towards AI

One of the most significant advantages of either building or purchasing an AI PC is that you’ll be able to upgrade most of the components yourself as you see fit. So, for example, if you need more RAM, you can go from 16 GB to 32 GB, 64 GB, 128 GB, etc. — depending upon the limitations of your motherboard. The same applies to upgrading your GPU. For instance, if your rig had an RTX 3060, and NVIDIA comes up with newer GPUs in a couple of years, you can easily swap them and future-proof your rig even more.

Another considerable advantage of going for an AI PC instead of an AI laptop is that you’ll be able to perform more power-hungry consuming tasks very well without needing to worry too much about the cooling capabilities of the AI rig. NVIDIA also has more possibilities for CUDA-Enabled GPUs for desktops, such as the RTX AXXXX series and the Quadro RTX options. You can see the complete list of the AI-powered GPUs in this list by NVIDIA Developer.

GPU accelerated libraries by NVIDIA AI.
GPU-Accelerated Libraries for AI and HPC | Source: NVIDIA [15]

But buying an AI PC rig also has disadvantages, for instance, if you travel a lot, you may find that purchasing a deep learning laptop would be more convenient for you, due to the amount of travel that you’ll be doing, and that for the most part, if you buy a suitable rig, you’ll be able to perform most AI-related tasks well on either of the two.

For data leaders who have more ample budgets and needs, having both an AI workstation and an AI laptop would make more sense due to the flexibility it offers to have both at your disposal. And for every AI task you can think of, including running powerful models in TensorFlow, PyTorch, Jupyter, CUDA, cuDNN, and so on.

We continue to receive a large number of emails from AI enthusiasts asking for the best AI rigs. So we made this list of the best PCs for AI projects. Please contact us at pub@towardsai.net if you have any suggestions to add to the list — we’ll be updating this article as we come up with newer and better AI workstations to power your every need.

Disclosure: Our editorial team at Towards AI writes authentic and trustworthy reviews and may receive a small compensation on products we select to support Towards AI’s efforts. For this article, as an Amazon Associate, Towards AI may receive a small commission from qualifying purchases made from it (at no extra cost to the buyer). For feedback, questions, or concerns, please email us at pub@towardsai.net.

📚 Check out our editorial recommendations for the best deep learning laptop. 📚

For Budgets under $ 1,000.00 ↓

In a moderate-budget AI — PC build, you will need to look for a processor to handle complex operations, such as Jupyter Notebooks. An Intel recommendation would be to use an i3 to i5, between the 10x to 11x F or K series, i.e., an Intel Core i5–11400F. The F and K series processors don’t have an integrated GPU, and they can perform most AI tasks competently. In addition to the Intel CPUs, we recommend checking out AMD CPUs as well since these have been reported to perform similarly in more economical price budgets.

Best deep learning workstations, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

CyberpowerPC

Best PC under $ 1k. Ideal for data leaders who care about Intel processors, suitable RAM size, fair expandability, and RTX GPUs under a $ 1k budget.

Specs:

  • Processor: Intel Core i5–11400F up to 4.5 GHz.
  • Memory: 8 GB DDR4.
  • Hard Drives: 500 GB NVMe SSD.
  • GPU: NVIDIA GeForce RTX 2060 6 GB.
  • Computing Power: 7.5 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.

Grab one on Amazon

For Budgets ~ $ 1500

If you can stretch your budget a bit, we strongly recommend pursuing an RTX 30x PC and future-proof your rig even more. Such as this Amazon bestseller:

Best deep learning desktops, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

Skytech Shiva

Best PC approximating $ 1.5k. Ideal for data leaders who care about AMD processors, suitable RAM size, RTX 30x GPUs, and lots of expandability.

Specs:

  • Processor: AMD Ryzen 5600X 3.7GHz
  • Memory: 16 GB DDR4.
  • Hard Drives: 1 TB NVMe SSD.
  • GPU: NVIDIA GeForce RTX 3060 Ti 8GB.
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Power supply: 600w

Grab one on Amazon

For Budgets ~ $ 2000.00

Best deep learning PCs, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

CUK Mantis

Best PC approximating $ 2k. Look at this beauty, the expandability, the motherboard, the liquid cooling — it leaves us in “aw.” This AI rig is ideal for data leaders who care about future-proofing their AI PCs, want the best in processors, large RAM, expandability, and RTX 30x GPUs. If you’d like to go with a better GPU but a bit less RAM, check out this SkyTech AI rig, offering an RTX 3070 Ti but with 16GB of RAM.

Specs:

  • Processor: Intel Core i9 K-
  • Memory: 32 GB DDR4.
  • Hard Drives: 512 GB NVMe SSD + 2 TB HDD.
  • GPU: NVIDIA GeForce RTX 3060 Ti 12GB.
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Power supply: 600w

Grab one on Amazon

For Budgets ~ $ 3k

Best deep learning PCs, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

CUK Omen 30L

Best PC under $ 3k. Beautiful AI rig, this AI PC is ideal for data leaders who want the best in processors, large RAM, expandability, an RTX 3070 GPU, and a large power supply.

Specs:

  • Processor: Intel Core i9 10900KF.
  • Memory: 32 GB DDR4.
  • Hard Drives: 1 TB NVMe SSD + 2 TB HDD.
  • GPU: NVIDIA GeForce RTX 3070 8GB.
  • Computing Power: 8.6 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Power supply: 750w

Grab one on Amazon

Monster AI Rigs — Unlimited Budgets!

Best deep learning rigs, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

CUK Continuum

[Tie] Best AI workstation. Beautiful AI rig, this AI PC is ideal for data leaders who want the best of the best but are inclined toward an AMD processor.

Specs:

  • Processor: AMD 9 5950X 3.4GHz up to 4.9GHz
  • Memory: 64 GB DDR4.
  • Hard Drives: 1 TB NVMe SSD + 3 TB HDD.
  • GPU: NVIDIA GeForce RTX 3090 24GB.
  • Computing Power: 7.5 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Power supply: 750w

Grab one on Amazon

Best deep learning desktops, power your every AI need, from machine learning, deep learning, and data science tasks.
Source: Amazon

CUK Mantis

[Tie] Best AI workstation. Beautiful AI rig, this AI PC is ideal for data leaders who want the best of the best but are inclined toward an Intel processor and a very large power supply. This AI workstation is the top of the line of our $ ~2k recommendation.

Specs:

  • Processor: AMD 9 5950X 3.4GHz up to 4.9GHz
  • Memory: 64 GB DDR4.
  • Hard Drives: 1 TB NVMe SSD + 2 TB HDD.
  • GPU: NVIDIA GeForce RTX 3090 24GB.
  • Computing Power: 7.5 [9]
  • Ports: 1x HDMI 2.0, 1x USB 3.1 Type-C, 2x USB 3.1, 1x USB 2.0.
  • OS: Windows 11 Home.
  • Connectivity: WiFi 802.11ax, Gigabit LAN (Ethernet), Bluetooth.
  • Power supply: 850w

Grab one on Amazon

Conclusion

We hope you find this list helpful in searching for an AI workstation for deep learning, machine learning, and data science projects. If you come across any phenomenal AI workstations, such as those mentioned in this list, please let us know by emailing us. If, instead of buying an AI desktop, you’d like to build one, stay tuned. We’ll be releasing a new list of recommendations with the best components to buy for your AI-powered workstation.

We’d also love to show our readers how to build an AI rig end-to-end. So if you represent a supplier and would like to sponsor an upcoming tutorial showing our audience how to build an AI rig, please feel free to contact us.

Thank you for reading!

References

[1] RTX Performance AI Rigs, Amazon, https://www.amazon.com/s?k=rtx+64gb+desktop&ref=nb_sb_noss_2

[2] Intel 10750H Q2 2020, Intel, https://www.intel.com/content/www/us/en/products/processors/core/i7-processors/i7-10750h.html

[3] Intel 9750H, Intel, https://www.intel.com/content/www/us/en/products/processors/core/i7-processors/i7-9750h.html

[4] AMD Ryzen 7 7800H, AMD, https://www.amd.com/en/products/apu/amd-ryzen-7-4800h

[5] Intel 10980 HK, Intel, https://ark.intel.com/content/www/us/en/ark/products/201838/intel-core-i9-10980hk-processor-16m-cache-up-to-5-30-ghz.html

[6] Intel 10875 HK. Intel, https://ark.intel.com/content/www/us/en/ark/products/202329/intel-core-i7-10875h-processor-16m-cache-up-to-5-10-ghz.html

[7] RTX 2080 vs. AMD Radeon Pro 5500M, User Benchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-2080-vs-AMD-Radeon-Pro-5500M/4026vsm960765

[8] RTX Performance Desktops, Amazon, https://www.amazon.com/s?k=rtx+64gb+desktop&ref=nb_sb_noss_2

[9] NVidia CUDA Geforce GPUS, Nvidia, https://www.nvidia.com/en-us/geforce/gaming-laptops/

[10] Nvidia CUDA Quadro GPUS, Nvidia, https://www.nvidia.com/object/quadro-for-mobile-workstations.html

[11] GPU UserBenchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-3060-vs-Nvidia-RTX-2070S-Super-Mobile-Max-Q/4105vsm1168355

[12] GPU UserBenchmark, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-3080-Laptop-vs-Nvidia-RTX-2080S-Super-Mobile-Max-Q/m1443565vsm1114823

[13] CUDA-Enabled GeForce and TITAN Products, https://developer.nvidia.com/cuda-gpus

[14] Max-Q Design, Nvidia, https://www.nvidia.com/en-us/geforce/gaming-laptops/max-q/

[15] NVIDIA CUDA-X

GPU-Accelerated Libraries for AI and HPC, https://www.nvidia.com/en-us/technologies/cuda-x/

Best Workstations for Deep Learning, Data Science, and Machine Learning (ML) for 2021 was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

 

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Established in Pittsburgh, Pennsylvania, US — Towards AI Co. is the world’s leading AI and technology publication focused on diversity, equity, and inclusion. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. Read by thought-leaders and decision-makers around the world. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. All of our articles are from their respective authors and may not reflect the views of Towards AI Co., its editors, or its other writers. | Information for authors → https://contribute.towardsai.net | Terms → https://towardsai.net/terms/ | Privacy → https://towardsai.net/privacy/ | Members → https://members.towardsai.net/ | Shop → https://ws.towardsai.net/shop | Is your company interested in working with Towards AI? → https://sponsors.towardsai.net

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Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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