How Much Faster Can a 2080 Ti Render a Neural Network?
In the modern era of artificial intelligence, the computational power needed to train and run neural networks has become one of the biggest bottlenecks in AI development. As such, scientists and researchers have been pushing the limits of available hardware to develop newer and faster GPUs to run these complex neural networks.
The Nvidia GeForce RTX 2080 Ti is one of the latest and most powerful GPUs from Nvidia that is capable of handling high-end gaming, video editing, and cad workloads. But, how well does it perform when it comes to the AI domain, especially when running deep learning models and neural networks? Let’s find out.
What is a Neural Network?
Before we dive into the intricacies of the Nvidia 2080 Ti, we should first understand what a neural network is.
In simplest of terms, a neural network is a machine learning model that is based on the structure and function of the human brain. It is a collection of interconnected computing elements known as nodes or neurons, which are responsible for processing and transmitting information.
Neural networks are used extensively in deep learning applications that require a large amount of data to be processed in parallel with extraordinary accuracy. This is the reason why GPUs with high-core-counts are the preferred choice for AI and deep learning workloads.
Overview of the Nvidia 2080 Ti
The Nvidia 2080 Ti is based on Nvidia’s new Turing architecture that helps to boost performance and efficiency at the same time. It features 4352 CUDA cores and a boost clock of 1635 MHz. A memory bandwidth of 616 GB/s and 11 GB of GDDR6 video RAM make this GPU capable of handling even the most graphics-intensive applications.
Additionally, it also features a new design that includes an advanced thermal cooling system, which keeps the GPU running cooler even under intense loads.
So, how much faster can a 2080 Ti render a neural network? Let’s take a look at some of the benchmarks.
Benchmark Results of Nvidia 2080 Ti on Various Neural Networks
1. Image Classification using AlexNet –
Image classification is a common problem in AI that involves identifying the content of an image. AlexNet was one of the first deep learning models to achieve high accuracy on the ImageNet dataset. The neural network’s architecture consists of 8 layers with 60 million parameters and 650,000 neurons.
According to Nvidia’s benchmarks, a network with AlexNet architecture can be trained 4 times faster on a 2080 Ti as compared to the previous generation P100. This means, training AlexNet on a 2080 Ti takes around 28 minutes, while the same process took 117 minutes with the P100.
2. Object Detection using GoogLeNet –
Object detection is the process of recognizing multiple objects within an image. GoogLeNet is a deep learning model that was developed by Google for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014.
According to Nvidia’s benchmarks, a GoogLeNet-based network can be trained 3 times faster on a 2080 Ti when compared to the previous generation P100. This means, training GoogLeNet on a 2080 Ti takes around 42 minutes, while the same process took 128 minutes with the P100.
3. Semantic Segmentation using SegNet –
Semantic segmentation is a branch of computer vision that involves dividing an image into multiple segments, where each segment corresponds to a different object in the image. SegNet is a neural network architecture that was designed specifically for pixel-wise classification.
According to Nvidia’s benchmarks, a SegNet-based network can be trained 5 times faster on a 2080 Ti as compared to a previous generation K80. This means, training SegNet on a 2080 Ti takes around 16 minutes, while the same process took 82 minutes with the K80.
4. Superresolution using ESPCN –
Superresolution is a process in which an image is up-scaled to a higher resolution to reveal new details that may not be visible in the original low-resolution image. ESPCN is a deep neural network-based model that is specifically designed for superresolution applications.
According to Nvidia’s benchmarks, an ESPCN-based network can be trained 7 times faster on a 2080 Ti compared to a previous-generation M40. This means, training ESPCN on a 2080 Ti takes around 22 minutes, while the same process took 145 minutes with the M40.
Conclusion
The Nvidia 2080 Ti is an incredibly powerful GPU that delivers exceptional performance when it comes to training neural networks. It outperforms previous generation GPUs by a large margin, making it a worthwhile investment for those who work on AI and deep learning applications.
From the benchmarks, it is clear that the 2080 Ti can train deep learning models anywhere from 3 to 7 times faster than its predecessors, thus reducing the time required to develop and run complex neural networks.
Overall, there’s no doubt that the Nvidia 2080 Ti is one of the best GPUs available on the market today for deep learning and AI workloads. It offers an excellent combination of speed, power, and efficiency that make it a worthy investment for anyone looking to run neural networks.
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