Best GPUs for Machine Learning for Your Next Project (2024)

Confused about which GPU to choose for your project? This blog covers the top 15 GPUs for machine learning and also guides you through the relevant factors to consider to make an informed decision when selecting a GPU for your next machine learning project.

According to JPR, the GPU market is expected to reach 3,318 million units by 2025 at an annual rate of 3.5%. This statistic is a clear indicator of the fact that the use of GPUs for machine learning has evolved in recent years. Deep learning (a subset of machine learning) necessitates dealing with massive data, neural networks, parallel computing, and the computation of a large number of matrices. All these methods use algorithms that process large volumes of data and transform it into usable software. This necessitates using a graphic card for processing to perform these tasks with deep learning and neural networks. GPUs come into play here. Using GPUs, you can break down complex tasks and perform multiple operations simultaneously. In addition, GPUs are ideal for developing deep learning and artificial intelligence models as they can handle numerous computations simultaneously.

Before diving into the best GPUs for deep learning or the best Graphics card for machine learning, let us know more about GPUs.

What is a GPU for Machine Learning?

A GPU (Graphic Processing Unit) is a logic chip that renders graphics on display- images, videos, or games. A GPU is sometimes also referred to as a processor or a graphics card. GPUs are used for different types of work, such as video editing, gaming, designing programs, and machine learning (ML). Thus, they are ideal for designers, developers, or anybody looking for high-quality visuals.

It is possible, however, to find a GPU integrated into a motherboard or in the daughterboard of a graphics card. Initially, graphic cards were only available on high-configuration computers. But today, most desktop computers use a separate graphics card with a GPU rather than one built into the motherboard for increased performance.

Why are GPUs better than CPUs for Machine Learning?

When it comes to machine learning, even a very basic GPU outperforms a CPU. But why so?

• GPUs offer significant speed-ups over CPUs when it comes to deep neural networks.

• GPUs are faster for computing than CPUs. This is because they are ideal for parallel computing and can perform multiple tasks simultaneously. While, CPUs perform sequential tasks. In addition, GPUs are ideal for the computation of Artificial Intelligence and deep learning applications.

• Since data science model training is based on simple matrix operations, GPUs can be used safely for deep learning.

• GPUs can execute many parallel computations and increase the quality of images on the screen.

• GPUs assemble many specialized cores that deal with huge data sets and deliver massive performance.

• A GPU devotes more transistors to arithmetic logic than a CPU does to caching and flow control.

• Deep-learning GPUs provide high-performance computing power on a single chip while supporting modern machine-learning frameworks like TensorFlow and PyTorch with little or no setup.

How do GPUs for Deep Learning Work?

Graphical Processing Units (GPUs) are built explicitly for graphics processing, which requires complex mathematical calculations running parallel to display images on the screen. A GPU receives graphic information such as image geometry, color, and textures from the CPU and executes them to draw images on the screen. Thus, this is how a GPU works to render images on the screen. This complete process of taking instructions to create a final image on the screen is known as rendering.

For instance, video graphics are made up of polygonal coordinates translated into bitmaps and then into signals shown on a screen. This translation necessitates massive processing power from the Graphics Processing Unit (GPU), which makes GPUs useful in machine learning, artificial intelligence, and other deep learning tasks that necessitate complex computations.

Why use GPUs for ML?

The next most important question to be answered is why use GPUs for machine learning or why are GPUs better for machine learning? Read on to find out!

The concept of deep learning involves complex computing tasks such as training deep neural networks, mathematical modeling using matrix calculations, and working with 3D graphics. All these deep learning tasks require choosing a reasonably powerful GPU.

A distinctive GPU not only helps with a high-quality image but also increases the efficiency of the CPU and achieves outstanding performance. Thus, investing in a top-quality GPU is best to speed up the model training process.

On the other hand, GPUs have dedicated video RAM (VRAM), which provides the required memory bandwidth for massive datasets while freeing up CPU time for different operations. They also enable you to parallelize training tasks by dividing them among processor clusters and carrying out computation operations simultaneously.

GPUs can perform simultaneous computations involved in machine learning. It is also important to note that you don’t need GPUs to learn machine learning or deep learning. They are essential only when you want to speed up your things while working with complex models, huge datasets, and a large number of images.

But how do you choose the best GPU for machine learning? Let’s find out!

How to Choose the Best GPU for Machine Learning

With this rapidly growing field of GPUs, various options are available in the market to keep the need of designers and data scientists. Therefore, it is essential to keep several factors in mind before purchasing a GPU for machine learning.

Factors to Consider When Selecting GPUs for Machine Learning

Below you will find the factors one must consider while deciding on the best graphics card for AI/ML/DL projects:

Compatibility

The GPU's compatibility with your computer or laptop should be your primary concern. Does your device's GPU perform well? You can also check the display ports and cables for deep learning applications.

Memory Capacity

The first and most important requirement for selecting GPUs for machine learning is more RAM. Deep learning necessitates intense GPU memory capacity. For example, algorithms that use long videos as training data sets require GPUs with greater memory. Compared to this, the fundamental training data sets function effectively on cloud GPUs with less memory.

Memory Bandwidth

Large datasets require a lot of memory bandwidth, which GPUs may provide. This is due to the separate video RAM (VRAM) found in GPUs, which lets you save CPU memory for other uses.

GPU’s Interconnecting Ability

The ability to connect multiple GPUs is closely related to your scalability and distributed training strategies. As a result, one should consider which GPU units can be interconnected when selecting a GPU for machine learning.

TDP value

GPUs can sometimes overheat, as indicated by the TDP value. They can heat up more quickly when they need more electricity to operate, so it is necessary to keep GPUs at a cool temperature.

Steam Processors

Steam processors, also called CUDA cores, are suitable for professional players and deep learning. A GPU with a high CUDA core increases work efficiency in deep learning applications.

Algorithm Factors Affecting GPU Use for Machine Learning

When it comes to GPU usage, algorithmic factors are equally important and must be considered. Listed below are three factors to consider when scaling your algorithm across multiple GPU for ML:

Data Parallelism

It is essential to consider how much data your algorithms will need to handle. If the data set is large, the chosen GPU should be able to function efficiently on multi-GPU training. If the data set is large, you must ensure the servers can communicate quickly with storage components to enable effective distributed training.

Memory Use

Another essential factor you must consider for GPU usage is the memory requirements for training datasets. For example, algorithms that use long videos or medical pictures as training data sets require a GPU with large memory. On the other hand, simple training data sets used for basic predictions need less GPU memory to work.

GPU Performance

The model's performance also influences GPU selection. Regular GPUs, for example, are used for development and debugging. Strong and powerful GPUs are required for model fine-tuning to accelerate training time and reduce waiting hours.

Best GPUs for Machine Learning in the Market

So, what makes GPU ideal for machine learning? This is because of a variety of reasons. GPUs are designed to do multiple computations in parallel, which is excellent for deep learning algorithms' highly parallel nature. They also contain a large amount of memory, which is useful for deep-learning models that require a lot of data.

It is also important to note that large-scale operations rarely buy GPUs unless they have their specialized processing cloud. Organizations running machine-learning workloads instead acquire cloud space optimized for high-performance computing. These cloud providers' platforms feature high-performance GPUs and fast memory. But from where do they buy the best GPU for AI training?

• GPU Market Players - Nvidia and AMD

There are two major players in the Machine Learning GPU market: AMD and Nvidia. There are a large number of GPUs used for deep learning. However, Nvidia makes the majority of the best ones. Nvidia dominates the market of GPUs, especially for deep learning and complex neural networks, because of their substantial support in the forum, software, drivers, CUDA, and cuDNN.

• Nvidia GPU for Deep Learning

NVIDIA is a popular choice because of its libraries, known as the CUDA toolkit. These libraries make it simple to set up deep learning processes and provide the foundation of a robust machine learning community using NVIDIA products. In addition to GPUs, NVIDIA also provides libraries for popular deep learning frameworks such as PyTorch and TensorFlow.

The NVIDIA Deep Learning SDK adds GPU acceleration to popular deep learning frameworks. Data scientists may use powerful tools and frameworks to create and deploy deep learning applications.

NVIDIA's downside is that it has lately set limits on when you may use CUDA. Due to these constraints, the libraries can only be used with Tesla GPUs, not with less costly RTX or GTX hardware. This has significant financial implications for firms training deep learning models. It is also problematic when you consider that, while Tesla GPUs may not provide considerably greater performance than the alternatives, the units cost up to ten times as much.

• AMD GPU for Deep Learning

AMD GPUs are excellent for gaming, but Nvidia outperforms when deep learning comes into the picture. AMD GPUs are less in use because of software optimization and drivers that need to be frequently updated. While on the Nvidia side, they have superior drivers with frequent updates, and on top of that, CUDA and cuDNN help accelerate computation.

AMD GPUs have extremely minimal software support. AMD provides libraries such as ROCm. All significant network architectures, as well as TensorFlow and PyTorch, support these libraries. However, community support for the development of new networks is minimal.

15 Best GPUfor Deep Learning 2023

Looking at the factors mentioned above for choosing GPUs for deep learning, you can now easily pick the best one from the following list based on your machine learning or deep learning project requirements.

5 Best NVIDIA GPUs for Deep Learning

Check out the best NVIDIA GPUs for deep learning below:

NVIDIA Titan RTX

NVIDIA Titan RTX is a high-end gaming GPU that is also great for deep learning tasks. Built for data scientists and AI researchers, this GPU is powered by NVIDIA Turing™ architecture to offer unbeatable performance. The TITAN RTX is the best PC GPU for training neural networks, processing massive datasets, and creating ultra-high-resolution videos and 3D graphics. Additionally, it is supported by NVIDIA drivers and SDKs, enabling developers, researchers, and creators to work more effectively to deliver better results.

Technical Features

• CUDA cores: 4608

• Tensor cores: 576

• GPU memory: 24 GB GDDR6

• Memory Bandwidth: 673GB/s

• Compute APIs: CUDA, DirectCompute, OpenCL™

NVIDIA Tesla V100

NVIDIA Tesla is the first tensor core GPU built to accelerate artificial intelligence, high-performance computing (HPC), Deep learning, and machine learning tasks. Powered by NVIDIA Volta architecture, Tesla V100 delivers 125TFLOPS of deep learning performance for training and inference. In addition, it consumes less power than other GPUs. NVIDIA Tesla is one of the market's best GPUs for deep learning due to its outstanding performance in AI and machine learning applications. With this GPU, data scientists and engineers may now focus on building the next AI breakthrough rather than optimizing memory usage.

Technical Features

• CUDA cores: 5120

• Tensor cores: 640

• Memory Bandwidth: 900 GB/s

• GPU memory: 16GB

• Clock Speed: 1246 MHz

• Compute APIs: CUDA, DirectCompute, OpenCL™, OpenACC®

NVIDIA Quadro RTX 8000

NVIDIA Quadro RTX 8000 is the world’s most powerful graphics card built by PNY for deep learning matrix multiplications. A single Quadro RTX 8000 card can render complex professional models with realistically accurate shadows, reflections, and refractions, providing users with rapid insight. Powered by the NVIDIA TuringTM architecture and NVIDIA RTXTM platform, Quadro provides professionals with the latest hardware-accelerated real-time ray tracing, deep learning, and advanced shading. When used with NVLink, its memory may be expanded to 96 GB.

Technical Features

• CUDA cores: 4608

• Tensor cores: 576

• GPU memory: 48 GB GDDR6

• Memory Bandwidth: 672 GB/s

• Compute APIs: CUDA, DirectCompute, OpenCL™

NVIDIA Tesla P100

Based on NVIDIA Pascal architecture, the Nvidia Tesla p100 is a GPU built for machine learning and HPC. Tesla P100 with NVIDIA NVLink technology provides lightning-fast nodes to reduce time to solution for large-scale applications significantly. With NVLink, a server node may link up to eight Tesla P100s at 5X the bandwidth of PCIe.

Technical Features

• CUDA cores: 3,584

• Tensor cores: 64

• Memory Bandwidth: 732 GB/s

• Compute APIs: CUDA, OpenCL, cuDNN

NVIDIA RTX A6000

One of the latest GPUs is the NVIDIA RTX A6000, which is excellent for deep learning. Based on the Turing architecture, it can execute both deep learning algorithms and conventional graphics processing tasks. The RTX A6000 also has Deep Learning Super Sampling as a feature (DLSS). This feature can render images at higher resolutions while maintaining quality and speed. A geometry processor, texture mapper core, rasterizer core, and video engine core are some of the other features of this GPU.

Technical Features

• CUDA cores: 10,752

• Tensor cores: 336

• GPU memory: 48GB

If you are specifically interested in a good GPU for LLM projects, then we recommend you check out The NVIDIA GeForce RTX 3050- one of the best GPU for LLM Project ideas.

5 Best GPUs for Deep Learning

Find below the top five GPU for deep learning examples:

NVIDIA GeForce RTX 3090 Ti

NVIDIA GeForce RTX 3090 Ti is one of the best GPU for deep learning if you are a data scientist that performs deep learning tasks on your machine. Its incredible performance and features make it ideal for powering the most advanced neural networks than other GPUs. Powered by NVIDIA ampere architecture, it provides the fastest speeds possible. With this NVIDIA Geforce RTX GPU, gaming enthusiasts may experience a maximum setting of 4K, ray-traced games at the fastest possible rates, and even 8K NVIDIA DLSS-accelerated gaming on monitors that support 8K 60Hz, as stated in HDMI 2.1.

Technical Features:

• CUDA cores: 10,752

• Memory Bandwidth: 1008 GB/s

• GPU memory: 24 GB GDDR memory

EVGA GeForce GTX 1080

EVGA GeForce GTX 1080 is one of the most-advanced GPUs designed to deliver the fastest and most-efficient gaming experiences. Based on NVIDIA’s Pascal architecture, it provides significant improvements in performance, memory bandwidth, and power efficiency. Additionally, it provides cutting-edge visuals and technologies that redefine the PC as the platform for enjoying AAA games and fully utilizing virtual reality via NVIDIA VRWorks.

Technical Features:

• CUDA cores: 2560

• GPU memory: 8GB of GDDR5X

• Pascal Architecture

ZOTAC GeForce GTX 1070

GeForce GTX 1070 Mini is one of the best GPU for deep learning due to its top-notch specifications, low noise levels, and small size. The GPU has an HDMI 2.0 connector that you may use to attach your PC to an HDTV or other display device. Additionally, the ZOTAC GeForce GTX 1070 Mini is compatible with NVIDIA G-Sync, which reduces input latency and screen tearing while enhancing speed and smoothness while developing deep learning algorithms.

Technical Features:

• CUDA cores: 1,920 cores

• GPU memory: 8GB GDDR5

• Clock speed: 1518 MHz

GIGABYTE GeForce RTX 3080

The GIGABYTE GeForce RTX 3080 is the best GPU for deep learning since it was designed to meet the requirements of the latest deep learning techniques, such as neural networks and generative adversarial networks. The RTX 3080 enables you to train your models much faster than with a different GPU. The GeForce RTX 3080 also offers a 4K display output, allowing you to connect multiple displays and design neural networks more quickly.

Technical Features

• CUDA cores: 10,240

• Clock speed: 1,800 MHz

• GPU memory: 10 GB of GDDR6

NVIDIA A100

The NVIDIA A100 GPU, built on the Ampere architecture, excels in powering deep learning tasks. It features Tensor Cores for efficient matrix operations, offers high memory capacities, supports NVLink for multi-GPU configurations, and boasts a rich AI software ecosystem. Data centers widely adopt it, and it is compatible with popular frameworks, making it a premier choice for accelerating the training of large neural networks.

Technical Features

• CUDA Cores: 6,912

• Clock Speed: 1.41GHz

• Thermal Design Power (TDP): 400 Watts

• Tensor cores: 432

What makes Python one of the best programming languages for ML Projects? The answer lies in these solved and end-to-end Machine Learning Projects in Python. Check them out now!

5 Best Budget GPUfor Deep LearningExamples

Here you will find a few examples of the best budget gpu for AI projects/ Deep Learning Project Ideas:

NVIDIA GTX 1650 Super

The NVIDIA GTX 1650 Super is one of the budget-friendly GPU offering decent performance for its price. With 4GB of GDDR5 memory and a reasonable number of CUDA cores, it was suitable for smaller deep learning tasks and well-supported by popular frameworks like TensorFlow and PyTorch. Its power efficiency and affordability made it an attractive option for budget-conscious users interested in deep learning or gaming.

Technical Features:

• CUDA cores: 1280

• GPU memory: 4 GB of GDDR6 VRAM

• Clock Speed: 1520 MHz

• GPU Chip: TU116-250 GPU chip

• Turing Architecture

GTX 1660 Super

One of the greatest low-cost GPUs for deep learning is the GTX 1660 Super. Its performance is not excellent as more costly models because it's an entry-level graphic card for deep learning.

This GPU is the best option for you and your pocketbook if you're just starting with machine learning.

Technical Features

• CUDA Cores: 4352

• Memory Bandwidth: 616 GB/s

• Power: 260W

• Clock Speed: 1350 MHz

NVIDIA GeForce RTX 2080 Ti

NVIDIA GeForce RTX 2080 Ti is the ideal GPU for deep learning and ArtificiaI Intelligence, both from a pricing and performance perspective. It has dual HDB fans that provide greater performance cooling, significantly less acoustic noise, and Real-Time Ray Tracing in games for cutting-edge, hyper-realistic visuals. The RTX 2080's blower architecture enables much denser system configurations, including up to four GPUs in a single workstation. In addition, the NVIDIA GeForce RTX 2080 Ti is a low-cost solution suited best for small-scale modeling workloads than large-scale training developments because of having less GPU memory per card (only 11 GB).

Technical Features

• CUDA cores: 4352

• Memory Bandwidth: 616 GB/s

• Clock Speed: 1350 MHz

NVIDIA Tesla K80

The NVIDIA Tesla K80 is the world’s most popular and budget-friendly GPU that significantly reduces data center costs by offering a great performance boost with fewer, more powerful servers. For example, if you've used Google Colab to train Mask RCNN, you'll have noted that the Nvidia Testa K80 is among the video GPUs that Google makes available. It is ideal for deep learning but isn't the perfect option for deep learning professionals for their projects.

Technical Features

• CUDA cores: 4992

• GPU memory: 24 GB of GDDR5

• Memory Bandwidth: 480 GB/s

EVGA GeForce GTX 1080

The EVGA GeForce GTX 1080 FTW GAMING Graphics Card, based on NVIDIA's Pascal architecture and equipped with a factory overclocked core, offers significant enhancements in performance, memory bandwidth, and power efficiency over the high-performing Maxwell architecture. Additionally, it provides cutting-edge visuals and technologies that redefine the PC as the platform for enjoying AAA games and entirely using virtual reality with NVIDIA VRWorks.

Technical Features

• CUDA cores: 2560

• GPU memory: 8GB of GDDR5X

• Memory Bandwidth: 320 GB/s

Key Takeaways

The GPU market will continue to grow in the future as we make innovations and breakthroughs in machine learning, deep learning, and HPC. GPU acceleration will always be helpful for students and developers looking to break into this sector, especially as their costs continue to decrease.

If you are still confused about picking the right GPU for learning machine learning or deep learning, ProjectPro experts are here to help you out. You can also schedule a one-to-one mentoring session with our industry experts to help you pick the right GPU for your next machine learning project while you kickstart your career in machine learning.

FAQs on GPU for Machine Learning

Which is the top GPU for deep learning?

NVIDIA, the market leader, offers the best deep-learning GPUs in 2022. The top NVIDIA models are Titan RTX, RTX 3090, Quadro RTX 8000, and RTX A6000.

Can GPUs be used for machine learning?

Yes, GPUs are capable of doing several calculations at the same time. This enables the distribution of training processes, which may significantly speed up machine learning activities. You can build many cores with GPUs that consume fewer resources without losing efficiency or power.

How Many GPUs are Enough For Deep Learning?

It all depends on the deep learning model being trained, the quantity of data available, and the size of the neural network.

Are Gaming GPUs good for machine learning?

Graphics processing units (GPUs), initially designed for the gaming industry, feature many processing cores and a significant amount of RAM on board. GPUs are increasingly employed in deep learning applications because they can significantly accelerate neural network training.