Best GPU for Machine Learning

Machine learning is a self-learning tactic to train the model or computer to perform certain tasks without being programmed. Its training phase is the most load-intensive one where the process takes the longest span, and an average computer can’t execute so much load. You may have to wait for hours or even weeks to complete the training phase, which diminishes productivity by extending and wasting time. But we can take help from the best GPU for machine learning to save our time and resources.

A graphics card powers up the system to perform all the load-taking tasks fastly by cooperating with the processor. It reduced latencies, improved the overall efficiency, optimized target tasks, and finished the computations in no time. The GPU enables you to train the models faster by parallelizing the tasks implementations and performing compute functions at the same time. Now you won’t have to wait for hours to see if the models have trained so we can pursue working on it. Instead, you will complete the operation and even multiple functions simultaneously.

However, you could only reach such a good level if you somehow find the best GPU required for the process. An incompatible one can worsen the situation by causing bottlenecks and reducing the system’s performance. So, you must spend a healthy amount of time researching and finding one. In addition, you can take help from some experts or read this post until the end since we have added everything you may need during the purchasing procedure – read the post and check the reviews of the eight best products.

Quick Shopping Tips

Form Factor: Graphics cards are available in many sizes, and each one has separate dimensions and accommodation. Remember, if you buy a card with the wrong size according to the PC case and motherboard, it will not fit properly and can’t be used as expected. So, make sure you take the measurements of every corner where you will hold the graphics card and buy one according to those dimensions.

Supporting software: In machine learning, trainers don’t directly program but need to run many to properly train and execute the instructions. You must ensure that the card supports machine learning libraries and some common frameworks such as PyTorch or TensorFlow. NVIDIA GPUs support all those more often since the CUDA toolkit has some GPU-accelerated libraries such as C, C++ compiler, and more.

VRAM size: Like the volatile memory, the graphics unit has a particular amount of memory called VRAM or video memory. The higher the total memory, the higher the system’s overall performance. Your system must have at least a 4GB graphics card for limitations brackets, but there is no such upper limit; you can go as high as possible. Remember, you must explore your usage and purchase a graphics card according to the requirement.

Clock speed: The clock speed or frequency is the speed of the graphics card. It is directly proportional to the performance since it reduces the execution time and boosts the system’s efficiency. In addition, every card has a particular base clock speed that can increase up to a certain level of boost speed, creating overclocking headroom. You must prefer a card with higher overclocking headroom and overall clock speeds.

Best GPU for Machine Learning at a Glance:

  1. ZOTAC GeForce GTX 1070 Mini
  2. Sapphire Radeon Pulse RX 580
  3. NVIDIA GeForce RTX 2080 Ti
  4. MSI Gaming GeForce GT 710
  5. NVIDIA GeForce RTX 2070
  6. MSI Gaming GeForce GTX 1660 Super
  7. NVIDIA GeForce RTX 3080
  8. ASUS ROG Strix Radeon RX 570

The Best GPU for Machine Learning You Can Buy Today


ZOTAC GeForce GTX 1070 Mini
ZOTAC GeForce GTX 1070 Mini (Image credit: Amazon)

ZOTAC GeForce GTX 1070 Mini

Best Selling GPU for Machine Learning

REASONS TO BUY

Excellent performance

Overclocking

Adequate thermal management

VR-ready graphics card

Noiseless fans’ movements

REASONS TO AVOID

Relatively pricey

Starting reviewing the list with the Zotac GeForce GTX 1070 Mini, an incredible addition to the tech market with enthusiast features and specifications. The GTX 10 series is built around the advanced NVIDIA Pascal architecture, so without missing a beat, it also uses the same empowering engine. I’m sure you know about this graphics architecture. If not, then listen; it’s an efficient and power-efficient design, and you don’t even have to use an insane cooling setup since it can self-cool itself with excellent thermal management—enough to call it the best GPU for machine learning and deep learning.

The card uses the GP104 graphics unit, famous for performance mixed with fun-making features, including immersive VR support. It is pretty much like the reference higher-end NVIDIA Founder Edition but with some alterations. You get an 8GB GDDR5 memory with an 8GHz memory clock carved on a 256-bit memory interface. It has 1920 CUDA cores and a 1506MHz that can bump up to a 1683MHz boost clock. To connect it, you can utilize three DisplayPorts 1.4, one HDMI 2.0b slot, and DL-DVI graphics output.

Interestingly, you can power four displays at a time with this card for multi-monitor support and VR setup. If we unfold the performance statistics, it can deliver 1440 graphics quality with 90+ fps and 4k with 60+ fps resolution–the reason why it is the best selling GPU for machine learning. It has an attractive majorly black color with some grey lining on the fan holders’ sides carved on an attractively bent structure. For cooling purposes, there are three 90mm EKO fans that rotate silently to not disturb you during your training sessions, an Ice Storm cooler, six copper heat pipes, and a wrap-around metal backplate.

Sapphire Radeon Pulse RX 580
Sapphire Radeon Pulse RX 580 (Image credit: Amazon)

Sapphire Radeon Pulse RX 580

Best Budget-Friendly GPU for Machine Learning

REASONS TO BUY

Affordable

Competitive performance

Power-efficient

Adequate thermal management

REASONS TO AVOID

No DVI-D port

Sapphire has been in the market for years with record-breaking inventions that led it to get a long name, especially when it comes to manufacturing AMD graphics cards. We have added many famous RX 580s in many of our reviews that were quite famous for excellent performance via many outstanding features. However, the story of the Sapphire Radeon RX 580 is a bit different since it’s renowned for having the most attractive price for tight-pocket users. The title of the best budget-friendly GPU for machine learning sits totally valid when it delivers the performance like the expensive Nitro+ cards.

The card is powered by the Polaris graphics architecture, the 4th generation GCN architecture with some future-proof technologies. If you look at the specifications chart, it has 8GB GDDR5 memory that can effectively reach up to 8000MHz memory speed, 2304 stream processors, and a 1366MHz of engine clock speed. In addition, the engine speed can go up to 1411MHz when you shift to silent mode powered by stable power, adequate cooling, and other features. Overall, the card is an excellent choice for those who want enough performance but at less price.

Want to know its graphics performance? Well, it suits perfectly to deliver 1080p to 1440p graphics resolution depending upon the game and other graphics-related settings. As far as the connectivity matters, there are three DisplayPorts and one HDMI port to utilize the card’s capabilities. The card is 9.5 inches in length and has a rugged aluminum-made backplate to help in protection and cooling. It features the Sapphire Dual-X cooling design, famous for its excellent airflow and thermal management, containing two 92mm fans with dust-repelling dual ball bearings.

NVIDIA GeForce RTX 2080 Ti
NVIDIA GeForce RTX 2080 Ti (Image credit: Amazon)

NVIDIA GeForce RTX 2080 Ti

Best High-End GPU for Machine Learning

REASONS TO BUY

Fastest for machine learning

Silent fans movement

Excellent cooling

Energy-efficient

REASONS TO AVOID

Pricey

Only one HDMI port

NVIDIA is the most-heard name in the graphics cards market with the most customers to its side. We have the NVIDIA GeForce RTX 2080 Ti, which is only 1% behind the next generation RTX 3080 and is way faster than the RTX 2080 variant in the series. The all-new Turing architecture powers it with the advanced RTX platform and a wide range of modern technologies. You can enjoy the spices of faster performance, real-time ray tracing, AI-based methodology, and more with this best high-end GPU for machine learning.

If we uncover the specification chart, the card uses a TU102 graphics unit with an 11GB GDDR6 memory is enough to deliver 616 GB/s bandwidth and 14 Gb/s memory speed with a 352-bit memory bus. You can take its 1350MHz base clock up to a 1545MHz boost clock to tackle a wide range of loads while training the models. Other important factors include 4352 CUDA core processors, 76T RTX OPS, 96 ROP units, 72 Ray Tracing cores, and 576 Tensor cores. In short, there is pretty much everything you may need during your ML processes and high-end gaming sessions.

Although the chip is made for high-end gaming performance, it can help you train the models in the same way since both tasks use the same technologies. It will save you time and energy, plus it will take all the multitasking responsibilities on itself. The card adorns a premium look with a brushed metal backplate for protection but a plastic front panel. Other than that, its efficient cooling system empowers you to perform all types of tasks. It uses a Tri-FROZR cooler design with three fans featuring a unique fan-blade design to effectively deliver thair to the massive heatsink.

MSI Gaming GeForce GT 710
MSI Gaming GeForce GT 710 (Image credit: Amazon)

MSI Gaming GeForce GT 710

Best Entry-Level GPU for Machine Learning

REASONS TO BUY

Affordable

Perfect for beginners

Easy to install

REASONS TO AVOID

Just enough performance

DDD3 memory

If you’re just stepping into the world of machine learning and deep learning, the MSI Gaming GeForce GT 710 is for you. It was launched back in 2014, and this date makes it slightly outdated, but it’s still the best entry-level GPU for machine learning. So, if you want some extra output from the existing PC, you should definitely try this graphics card. You can add up to three displays and use NVIDIA’s driver support, enhanced performance, some good-looking specifications, and a few impressive technologies. Moreover, it works soundlessly and can fit into all types of hardware for machine learning purposes.

Honestly, I don’t expect too much from such a type of entry-level graphics card. The GT 710 is based on the old GK208 chip powered by Kepler graphics architecture. It is manufactured on a 28nm TSMC processor and 768 CUDA cores, and a 2GB DDR3 type memory with 1600MHz memory speed based on a 64-bit memory interface. Apart from that, its engine can operate with a 954MHz core clock which is not like the current-era cards but complements the product’s price.

There are nine sensors to keep the monitor memory and VRM in check so you can take it up to its maximum performance level with this best GPU for machine learning and deep learning. You can connect three monitors simultaneously with the help of one display port, one HDMI connection, and a DVI-D slot. If we compare its 5.75 x 0.75 x 2.72 inches dimensions with the others in the market, the card falls in the smaller category. It occupies two expansion slots with such a size plus easy to install with a smaller form factor saving you time.

NVIDIA GeForce RTX 2070
NVIDIA GeForce RTX 2070 (Image credit: Amazon)

NVIDIA GeForce RTX 2070

Best Flagship GPU for Machine Learning

REASONS TO BUY

Outstanding synthetic performance

Plays 4k games

Competitively power-efficient

DLSS and Ray Tracing support

REASONS TO AVOID

No SLI support

The NVIDIA GeForce RTX 2070 delivers the ultimate performance being a mainstream Turning GPU and replaces the previous RTX 2060 series. You can expect an impressive level of speed and lower latencies when training your model during the machine learning sessions. Moreover, if you can’t afford the RTX 2080 Ti, this best flagship GPU for machine learning must be your first choice with similar performance benchmarks. The card is powered by the NVIDIA Turning architecture combined with the RTX platform and AI-based technologies.

It’s 100% convincing when the real-time Ray Tracing performance hits the 6 Giga Rays per second mark. You may say it is a downgraded version of the RTX 2080 with a TU106 processing chip. It has the same 8GB GDDR6 memory with 14 Gb/s memory speed and 448 GB/s memory bandwidth with a 256-bit memory bus. You can take its engine’s 1410MHz base clock up to a 1620MHz boost clock. The card also has 2304 CUDA cores which are slightly lower than 2304 in RTX 2080.

You can support up to four monitors with the card’s multi-monitor support and connect those with DisplayPort, HDMI, and DVI-D–an USB Type-C port is also given. The design presents a metallic look both in the form and in the back, decorated with a silver accent and lined-backplate at the back. Interestingly, the brand has redesigned the structure and installed a next-gen 6-phases power supply with two 13-blade fans grouped with a vapor chamber for quiet and ultra-cooling performance. Overall, the card has the same price as the GTX 1080 but offers way more performance, especially excellent ray tracing.

MSI Gaming GeForce GTX 1660 Super
MSI Gaming GeForce GTX 1660 Super (Image credit: Amazon)

MSI Gaming GeForce GTX 1660 Super

Best Affordable GPU for Machine Learning

REASONS TO BUY

Affordable

Overclocking and performance

Effective thermal management

Attractive design with slight RGB touch

Low-noise fans

REASONS TO AVOID

RTX and DLSS feature is not included

We have done deep research before writing this article and discovered some helpful pieces of information related to the tech market. Most importantly, a majority of the customers want a budget-friendly and mid-range type of product with good performance. If you’re one form such a group, the MSI Gaming GeForce GTX 1660 Super is the best affordable GPU for machine learning for you. It delivers 3-4% more performance than the NVIDIA’s GTX 1660 Super, 8-9% more than the AMD RX Vega 56, and is much more impressive than the previous GeForce GTX 1050 Ti GAMING X 4G.

It has a TU116 Graphics Processing Unit powered by the advanced Turing graphics architecture. The specifications sheet says that the card has 6GB GDDR6 type memory that can run with 14Gb/s memory speed based on a 192-bit memory interface. Also, it has 1408 CPU core processors, and its engine clock speed can go up to 1830MHz boost clock speed. If you use the multi-monitor support correctly, you can connect four displays simultaneously with the help of three DisplayPorts and one HDMI connection.

It not only plays well in the gaming session but can perform impressively to help your system train the models during deep learning and machine learning tasks. Undoubtedly, it will win your heart with exceptional performance and a reachable price tag. At the end of the review, I would like to add some light on its design and cooling structure. The card features a black look painted with a gunmetal gray color supported by a classy brushed metal backplate mixed with RGB lighting. The Twin Frozr 7 thermal design contains two fans, intelligent aerodynamics, and a massive heatsink.

NVIDIA GeForce RTX 3080
NVIDIA GeForce RTX 3080 (Image credit: Amazon)

NVIDIA GeForce RTX 3080

Best Premium GPU for Machine Learning

REASONS TO BUY

Tough build

Decent design

Two HDMI 2.1 ports

Excellent cooling

REASONS TO AVOID

Highest TDP for a single GPU

NVIDIA keeps improving its previous technologies and discovering new ones to match up with the tech requirements. We can see the same mastery in its RTX 3000 series, which carries numerous advancements to achieve 50% more performance than the previous RTX 2000 series. The NVIDIA GeForce RTX 3080 is the first third-party addition in the series and the second most-fastest card after the RTX 3090 graphics card. It’s a perfect mixture of high-end performance, fastest speeds, and excellent thermal management to reach its peak efficiency level.

Like the other advanced Nvidia graphics cards, this one is powered by the Ampere architecture along with second-generation Ray Tracing and third-generation Tensor cores. Moreover, it has 8704 CUDA cores and 10GB of GDDR6x memory with 19 Gb/s memory speed based on a 320-bit memory bus. You can utilize three DisplayPort and two HDMI 2.1 ports to support multi-monitor features to power four monitors simultaneously. This best premium GPU for machine learning is a perfect match to reduce the latencies while training the model.

ASUS has spent hours designing and manufacturing the card and embedding the military-grade components on the PCB sheet. The high-quality chokes, premium MOSFETs, and durable capacitors all are combined to provide a longer life to the product. If we look at the card, it has a unique look, a black mixed with silver accent, and the model name written on the front panel. Moreover, the brand has installed an excellent cooling design having three axial-tech fans, a massive heatsink, and six heat pipes for adequate thermal management.

ASUS ROG Strix Radeon RX 570
ASUS ROG Strix Radeon RX 570 (Image credit: Amazon)

ASUS ROG Strix Radeon RX 570

Best Performance GPU for Machine Learning

REASONS TO BUY

Good value for money

Packed with good-quality features

Faster than the ancestors

Operates quietly

REASONS TO AVOID

No NVlink support

At the end of the reviews section, we have the ASUS Rog Strix Radeon RX 570, which is the best performance GPU for machine learning and deep learning. There are two versions in the stack, but we have chosen one with higher performance and higher-end specifications. It has higher processing power, excellent boosting technology, faster memory, and more. If we compare it with the previous Strix RX 470 OC, the brand has intelligently improved its cooling solution, card layout, and overclocking since some users have faced some issues with the RX 470 OC.

If we look at the inside body of the card, it uses a Navi 14 graphics unit packed with 8GB GDDR5 memory that can run with 7000MHz memory speed and 224 Gb/s bandwidth with the help of a 256-bit memory bus. It has 2048 stream processors and a 1737MHz base clock speed that can increase to a 1845MHz boost clock. Interestingly, it supports connecting up to six monitors simultaneously and has one HDMI 2.0 port, one DisplayPort 1.4, and a pair of native DVI-D connections.

As far as the performance matters, it can deliver up to 1080p to 1440p high-quality graphics in major AAA titles games, plus it can help you boost up the machine learning process reducing the latencies. Remember, the card is built on a multi-layer PCB sheet along with high-quality MOSFETs, chokes, and other power elements to deliver optimal input power for efficient performance. Look-wise, it has RGB lighting decorating a black accent painted on a uniquely designed body having fans with two different logos in the center of each.

Conclusion

Machine learning puts an intense load on a system when training the model. The process can take hours and even days to feed all the data into the model and execute that to get the output. However, we can increase the overall productivity by putting the best GPU for machine learning into the system and reducing the time. A graphics unit takes the graphics load from the processor and separately performs its functionalities to compute the data in no time. Indeed. If you’re really into machine learning, having an excellent card is your responsibility.

It’s easy to know that you need a graphics card but very complicated to find one of the best Graphics card for machine learning. You need to put a reasonable amount of time into uncovering a compatible graphics card for your system. However, we have tried our best to introduce you to at least one best-suiting product to you, fulfilling all your needs. The buying guide section at the beginning of the article helps you understand some must-have factors in the compatible card. On top of that, the list contains all types of products for every user’s needs. So find your pain points and start seeing the best product according to the usage.

Frequently Asked Questions

Which is the best GPU for machine learning?

The GeForce RTX 3080 is the first third-party addition in the series and a great one in the tech market. It has 10 GB GDDR6x type memory which is enough to help you in training the models in no time. Further, the card has a challenging build with a stunning design along with excellent cooling and multi-monitor support.

Is a 2GB GPU enough for deep learning?

The VRMA size depends upon your usage, but still, 2GB is too less. You must invest in at least 4GB of graphics memory if you want to train your model in a reasonable time. Additionally, if you go beyond the limits, you can go up to any maximum GPU size since the memory is directly proportional to the performance.

Should I buy a GPU for deep learning?

The short answer is yes. You must invest some bucks into a good-quality graphics card to support the model’s learning process. It helps in reducing latencies, enhancing efficiency, and bringing the performance up to an optimal level.

Arslan Ashraf
Arslan Ashraf is Computer Expert. He developed his passion for reviewing the latest tech products. He likes to review every single piece of Hardware. If you have any questions related to Tech Products. He'll be happy to answer you.