As we walk into the age of AI, there is an exponential rise in the demand for GPU. The not-so-old method of parallel computing is applied to process computations in GPUs. Moreover, with the availability of very high numbers of ALUs or processing units, GPUs have become very suitable for powerful computations in AI. Furthermore, with the recent advent of Deep Learning in the current decade, most of the Deep Learning frameworks, including vastly popular TensorFlow, Pytorch, Theano, etc., enable advanced optimization of computations with GPU. Currently, a vast number of GPUs are available, with many differences in their features, like no. of processing units, memory capacity, clock frequency, etc. Here, we will discuss the best GPUs for deep learning and their pros and cons.
This GPU is one of the old horses of this game. Released in the latter months of 2019, it became popular in no time due to its fast memory architecture and high clock speed. It has 8GB memory of the fastest GDDR6 bunch with 15.5 Gbps speed. Its clock speed of 1650 MHz makes fast processing of vast computations required for training and inferencing from a big neural network. It is based on Turing technology, which enables ray tracing to create realistic images. It has a low TDP of 250 watts, making it overheat only occasionally. The only drawbacks it has is the 8GB memory, which disables to train big neural networks in large batch sizes, resulting in some reduction of model performance. Its only 368 Tensor cores do not allow users to build a massive model.
Released in July 2019, this GPU solves most problems of the RTX 2080. It has almost similar built to the RTX 2080 counterpart, except it has 11 GB of memory, which enables to train big neural networks in decently large batch sizes. It has 544 Tensor cores, which allow users to build a very big model. The only drawback it has is its slightly lesser clock speed of 1400 MHz makes a bit slower performance than RTX 2080.
Released in September 2020, this GPU has been aimed to solve most issues of deep learning. Powered by Ampere architecture, it supports high-speed GDDR6X memory and 3rd generation Tensor cores with very high bandwidth. With around 9000 Cuda cores and a massive clock speed of 1800 MHz, it allows users to train very big neural networks quickly. It has a memory of 10 GB, which enables it to make decent batch sizes that do not degrade model performance.
Released in March of this year, this GPU has the capabilities of the highest bracket available. With a dedicated ray tracing engine that’d enable the creation of highly realistic images by generative networks. Moreover, with 10,752 available cores, it has become one of the fastest available GPUs. The memory of 24 GB enables training very large network architecture with large batch sizes, making it highly suitable for state-of-the-art research.
Released in December 2018, this GPU is another old horse of this game. It is a little slower than the RTX 20 series, with 1350 MHz clock speed. However, its high memory capacity of 24 GB enables it to train very big neural networks in large batch sizes.
This is the first GPU made from Gigabyte with Ampere architecture. Released in September 2020, it’s currently one of the most powerful GPUs. Its 10GB of GDDR6 memory would enable to train of big networks in large batch sizes, but with a little slower performance in terms of reading and writing from memory. However, its 10,240 Cuda cores and 1800 MHz clock speed make up for a little slower memory interaction.
Also released in September 2020, a few differences it has to its Gigabyte counterpart is its 10 GB GDDR6X memory, enabling super-fast reading and writing from memory. It has 8960 Cuda cores and 1800 MHz clock speed, making the performance jaw-droppingly fast. The cooling facilities available for this GPU are also unique and rarely allow the device to overheat.
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I'm Arkaprava from Kolkata, India. I have completed my B.Tech. in Electronics and Communication Engineering in the year 2020 from Kalyani Government Engineering College, India. During my B.Tech. I've developed a keen interest in Signal Processing and its applications. Currently I'm pursuing MS degree from IIT Kanpur in Signal Processing, doing research on Audio Analysis using Deep Learning. Currently I'm working on unsupervised or semi-supervised learning frameworks for several tasks in audio.