A guide to the best GPUs for deep learning in 2018. Includes recommendations for both budget and high-end cards.
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Graphics processing units (GPUs) are specialized processors designed to increase performance for certain workloads, such as Deep Learning. Most consumers are familiar with GPUs only in the context of video games, but they have a much wider range of uses.
GPUs first became widely used in scientific and academic applications in the early 2000s, when they were used to accelerate 3D graphics rendering. In 2009, CUDA was introduced, which allowed general purpose computing on GPUs (GPGPU). This opened up a whole new range of potential applications for GPUs, including machine learning.
Since then, GPUs have become increasingly important for Deep Learning due to the fact that they can provide orders of magnitude speedups over CPUs for many Deep Learning tasks. For example, training a model that takes days on a CPU can often be completed in hours on a GPU.
There are two main types of GPUs available on the market today: NVIDIA and AMD. NVIDIA is currently the market leader in GPUs, with their GTX and RTX lines being the most popular choice among consumers. AMD has recently released their own line of GPUs for Deep Learning called the Radeon Instinct MI series, which aims to compete with NVIDIA’s offerings.
In this article, we will be comparing the best NVIDIA and AMD GPUs for Deep Learning in 2018.
What is Deep Learning?
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. By using artificial neural networks, deep learning algorithms can learn complex patterns in data; for example, they can learn to recognize objects in images or identify the speaker in an audio recording.
What are GPUs?
GPUs (Graphics Processing Units) are specialized computer chips that were originally designed to accelerate graphics rendering for gaming and other 3D applications. However, GPUs have also proven to be very efficient at performing the complex matrix operations required for deep learning.
GPUs are now being used extensively for training deep learning models. In fact, many of the largest and most successful deep learning projects, such as Google Brain and OpenAI, use GPUs for training their models.
There are two main types of GPUs: discrete GPUs and integrated GPUs. Discrete GPUs are typically faster and more powerful than integrated GPUs, but they also require more power and are more expensive.
The best GPU for deep learning in 2018 is the Nvidia Titan V. This GPU is based on the Volta architecture and has a massive amount of computing power, making it ideal for training large deep learning models. It is also very expensive, so it may not be the best option for everyone.
Other excellent GPUs for deep learning include the Nvidia GeForce GTX 1080 Ti, the Titan Xp, and the Titan X (Pascal). These GPUs are all based on the Pascal architecture and offer excellent performance at a relatively reasonable price point.
The Best GPUs for Deep Learning in 2018
As AI becomes more widespread, the need for powerful GPUs is also on the rise. Here are some of the best GPUs for deep learning in 2018, based on performance, features, and price.
NVIDIA GeForce GTX 1080 Ti – The Best Overall GPU for Deep Learning
The GTX 1080 Ti is the most powerful consumer GPU on the market, and it’s ideal for deep learning. With 11GB of GDDR5X VRAM, it can handle even the most complex neural networks. It also has a large thermal design power (TDP) of 250W, so it requires a beefy power supply to run properly. The GTX 1080 Ti is currently retailing for around $1000 USD.
AMD Radeon RX Vega 64 – The Best Budget GPU for Deep Learning
If you’re looking for a more budget-friendly option, the RX Vega 64 is a good choice. It’s not as powerful as the GTX 1080 Ti, but it’s still considerably faster than previous-generation cards like the GTX 1060. It also has a TDP of only 195W, so it doesn’t require as much power to run. The RX Vega 64 can be found for around $700 USD.
NVIDIA Tesla P100 – The Best Enterprise GPU for Deep Learning
If you need an enterprise-grade GPU for deep learning applications, the Tesla P100 is worth considering. It’s much more expensive than consumer GPUs like the GTX 1080 Ti, but it delivers significantly higher performance. With 16GB of HBM2 VRAM and a TDP of 250W, it can handle even the most demanding neural networks. The Tesla P100 retails for around $8000 USD.
When it comes to the best GPUs for deep learning in 2018, there are a few things to keep in mind. The first is that there is no one-size-fits-all answer – what works for one person may not be the best for another. Second, deep learning is a complex and ever-evolving field, so what may be the best GPU today may not be the best GPU in a year or two.
With that said, there are a few GPUs that stand out as being particularly well suited for deep learning. The NVIDIA GeForce GTX 1080 Ti is one of the most powerful consumer GPUs on the market, and is a great choice for those looking to get into deep learning. For those with a bit more budget to spend, the NVIDIA Titan Xp is an even better option, offering even more power and performance.
Finally, it’s important to remember that deep learning is a very computationally intensive task, so even the best GPUs will likely need help from a powerful CPU. For this reason, it’s often a good idea to pair a high-end GPU with a high-end CPU when building a deep learning system.
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