This article provides a consolidated list of the best GPUs for deep learning in 2021. In this article, you will also learn about GPU Accelerators and how to choose one.
There has been a great surge in the demand for deep learning and artificial intelligence solutions in the industry majorly due to the need for improved security, a need for more intelligent customer service agents and increasing need for automation in routine tasks.
GPUs are a hugely popular choice for AI because they can process data multiple times faster than CPUs and can deliver results without compromising on accuracy. They also operate at a much lower energy cost and have built-in cooling features.
A GPU is a graphics processing unit with its own processor. It is a specialised computing circuit designed to rapidly calculate mathematical equations. Today, GPUs find a great use case for accelerating applications like deep learning model training.Source
GPUs are most often found in PCs, but they are also used in video game consoles and mobile devices. GPUs have been designed to give an incredible boost to the speed of the system by performing the bulk of the work in parallel. Resulting in 10 to 100 times more performance efficiency.
Today, GPUs find a great use for deep learning or artificial intelligence programs that have many operations being performed at once, such as training neural networks or rendering 3D scenes (graphics). For this reason, GPUs are the best option for running these types of tasks because they can perform these operations more efficiently than CPUs can.
The training phase is the most time-consuming and resource-intensive part of most deep learning systems. This phase may be completed in a fair period of time for models with fewer parameters, but as the number of parameters rises, so does the training time.
This has two consequences:
1. Your resources are engaged for a longer period of time, and
2. Your team is left waiting, losing precious time.
Graphics processing units (GPUs) can help you save money by allowing you to run models with a large number of parameters fast and efficiently. This is due to GPUs' ability to parallelize workloads by spreading them across clusters of processors and executing computing operations concurrently. This architecture method is also known as SIMD.
SIMD stands for Single instruction multi data. When a single program is divided into sub-processes (or sometimes not even divided) to work in parallel on multiple data streams or multiple threads then it is called SIMD or SIMT architecture.
GPUs are perfect for handling large amounts of data with low latency & high speeds which makes them the best choice of accelerators for deep learning applications. However, there are several factors that define a GPUs capability to handle different workloads like deep learning model training or 3D graphics rendering.
Some of the most important features of a GPU for deep learning are:
The first and most important feature of a GPU for deep learning is the memory. It must be at least 8 - 12 GB GDDR5, otherwise it will not work as expected. The memory also determines how many concurrent operations you can do at once.
Here is a quick list of the best GPUs for deep learning in 2021 considering their computing and memory optimizations to deliver state of the art performance for training and inferring your DL models:
Read below for in-depth clarity on what makes these GPUs so powerful for deep learning.
Feel free to skip this part if you already have an idea on this and simply jump to the next part where we tell about how you can get access to these powerful GPUs for your deep learning needs!
The GeForce RTX 2080 Ti is a budget-friendly GPU designed to support use cases like deep learning, video editing, gaming and 3d modeling. It carries:
It has turned out to be 73% as fast as Tesla V100 GPU.
RTX 2080 Ti is a high-performance GPU for AI-driven tasks, with the best price/performance ratio. The only limitation is the size of its VRAM - for projects that require a lot of data processing, it may be better to choose a different GPU. In order to work well on the RTX 2080 Ti, you'll have to use smaller batches.
NVIDIA TITAN RTX is one of the most powerful graphics cards ever manufactured for the PC with a very beautiful design and a strong build. It enables researchers and developers to perform compute-intensive workloads right from the access of their local workstations and desktops.
NVIDIA TITAN RTX offers:
For deep learning it is:
Overall it gives a decent upgrade over RTX 2080 Ti in terms of memory and performance but comes at a very hefty price tag.
If you are satisfied with the performance delivered by NVIDIA TITAN RTX and need that extra pump in GPU memory then simply go for it. The NVIDIA TITAN RTX is intended for use by academics, developers, and artists.
The NVIDIA Tesla V100 was one of the first GPUs designed with a very strong intent for machine learning, deep learning, and high-performance computing (HPC).
The V100 is powered by NVIDIA Volta architecture, which was the the first to introduce tensor cores (TCs), a type of core specifically tailored for machine learning and deep learning workloads. Resulting in 4x performance gains over Pascal architecture for tasks that can make of Tensor cores.
NVIDIA Tesla V100 comes packed with:
Data scientists are tackling more complex AI challenges, such as voice recognition, training virtual personal assistants, and teaching self-driving cars. These challenges require training deep learning models in a reasonable period of time and GPUs like Tesla V100 can help in making this training process a lot faster.
The only issue is that Tesla V100s carry a huge cost with them and by design can only run in data center server racks.
The RTX 3090 GPU is built on Nvidia's latest Ampere architecture and comes packed with:
When compared to industrial grade GPUs such as the Tesla V100, the RTX 3090 is a "bargain" at about half the price. Great for gaming as well as professional tasks such as training for deep learning. With 24GB of GPU memory, the RTX 3090 is the clear winner in terms of GPU memory.
The RTX 3090 offers the most CUDA cores at this price point (10496) and one of the highest memory bandwidths (936 GB/S). If you're running training ML models, editing videos or modeling 3D animations then a high-powered GPU like this would really help reduce the time to solution.
The NVIDIA A100 GPU provides unrivaled acceleration at any scale, enabling the world's most performant elastic data centers for AI, data analytics, and HPC. Powered by NVIDIA's latest Ampere Architecture, A100s outperform previous generation by up to 20 times and can also be divided into seven GPU instances to dynamically respond to changing workloads.
The NVIDIA A100s boast:
The A100s carry the world's fastest memory bandwidth of over 2 terabytes per second (TB/s) to support the biggest models and datasets.
On the A100, researchers were able to cut a 10-hour double-precision simulation to under four hours by combining it with 80GB of the fastest GPU memory.
It takes a lot of computational power to train complex AI models with 1000s of neurons and hidden layers. A computationally heavy model like BERT can be trained in under a minute with 2,048 A100 GPUs, which is a phenomenal feat in its own.
GPUs are not easy to find. They are often difficult to purchase and expensive to rent. Some GPUs may suit your deep learning needs but they’d end up being prohibitively expensive for your bank.
We have mentioned below few options that for you to quickly get GPUs for deep learning needs:
If you are new to decentralized computing then checkout Q Blocks GPU instances for deep learning - a decentralized computing platform enabling access to high-end GPU instances for machine and deep learning at upto 10X low cost.
Q Blocks enables access to underutilized GPU computing resources in a secure and very cost-effective way for use cases like deep learning and machine learning model training, data science, 3d rendering, NFT(Non-Fungible Token) art creation, and much more.
The computing instances on Q Blocks platform are pre-configured with desired AI frameworks like Tensorflow, PyTorch, and Keras, and then you may use Jupyter Notebooks out of the box to swiftly develop, train, and deploy AI models.
At the end it is your choice for going with a specific GPU provider and for going with a particular GPU for deep learning models.
Deep learning and machine learning tasks need a high level of processing power in order to progress rapidly. In comparison to CPUs, GPUs can offer more processing power, better memory bandwidth and more parallelism. Budget and expertise should be considered when deciding between on-premise, cloud and decentralized GPU resources.
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