Choosing the Right GPU For Deep Learning

Deep learning is a set of machine learning algorithms that model high-level abstractions in data using architectures consisting of multiple non-linear transformations. The technology is based on artificial neural networks (ANNs). These ANNs are fed with learning algorithms and increasing amounts of data to improve the efficiency of the learning processes. The larger the amount of data, the more efficient the process.

The most time-consuming, labour-intensive and costly phase of a deep learning system is the training phase. Data scientists often spend hours or days waiting for training to be completed. Using GPUs for deep learning significantly reduces training time because the technology allows AI computations to run in parallel. When evaluating GPUs, consider multi-GPU connectivity, available support software, licensing, data parallelism, GPU memory usage, and performance.

In this article, we will look at why GPUs are so important for deep learning, how to choose the right GPU for your needs.

What are GPUs?

GPUs are microprocessors designed to perform specific tasks. They enable parallel processing of tasks. The first reason for using a GPU is that deep neural network (DNN) output on a GPU is 3-4 times faster than on a central processing unit (CPU) at the same price. The second reason is that offloading some of the load from the CPU allows more jobs to be run on the same instance, reducing the overall load on the network.

A typical deep learning pipeline with GPUs includes:

  • Data preprocessing (CPU)

  • DNN execution: training or inference (GPU)

  • Data post-processing (CPU) 

Why are GPUs important for deep learning?

Deep learning uses neural networks for tasks such as image recognition, object detection, image segmentation and many other tasks.

A deep neural network requires a huge dataset to tune billions of parameters to achieve good performance. The size of the training data set is directly proportional to the training time. This training time can be reduced from weeks to days and days to hours by taking advantage of the parallel processing capabilities of the GPU.

Memory. The GPU contains a memory called VRAM (video random access memory). When we train a neural network, we provide a set of data in the form of packets. The size of these packets depends on the amount of video memory in the GPU, so a GPU with more video memory can support a large packet size, reducing the time required for training.

Parallelism. A deep neural network is structured uniformly, each layer consists of thousands of identical artificial neurons performing the same operations. Therefore, the structure of a deep neural network may well handle the types of computations that a graphics processor can efficiently perform.

Timing. A shallow neural network with a small data set can be easily trained on a CPU. Whereas a deep neural network with billions of parameters and a huge dataset would take weeks or months to train on a CPU. This time can be easily reduced by training the neural network on GPU instead of CPU.

Why Use Cloud GPU?

Cloud GPUs have become popular in the data science community. Using GPU instances in the cloud is much simpler because users do not have to pay upfront costs for installation, management, maintenance and upgrades.

Cloud platforms provide all the services needed to use GPUs in computing, and the providers are responsible for managing the infrastructure. This allows deep learning professionals to focus on their core tasks and increase productivity.

Using cloud GPUs saves time and is more cost effective. This lowers the barrier to building a deep learning infrastructure, making it possible for smaller companies to start using the technology.

Popular GPUs for ML 

Hash generation, 2D graphics and 3D modelling require different amounts of resources and software. Depending on the application, we offer servers with NVIDIA® Tesla® V100, P100, M60, M40, RTX 3060-3090 and RTX 4060-4090 GPUs. If you have a large project with 3D models or need to perform complex calculations quickly, renting a render farm based on Tesla M40 GPUs may be the right solution for you.

Which GPU models are most commonly used for neural network training projects? We could mention a large number of graphics cards, but we will focus on just three.

NVIDIA GeForce RTX 3090

The NVIDIA GeForce RTX 3090 is a high-performance graphics card for gamers that can be successfully used in ML. Its 24GB of memory and high processing power make it an attractive choice for training models.

AMD Radeon Instinct MI100

AMD also offers powerful GPUs such as the Radeon Instinct MI100, which offers high performance for floating point calculations.

NVIDIA Tesla V100

The NVIDIA Tesla V100 is one of the most powerful and efficient GPUs for training deep learning models. This GPU has a number of tensor cores and is able to provide high performance when working with large datasets. Let's take a closer look at this model, because it is the model used to run a significant proportion of projects in the world of machine learning. It is NVIDIA chips that are currently driving the AI revolution.

What is the special feature of the NVIDIA Tesla V100 based GPU server for training neural networks?

Tensor cores. The NVIDIA Tesla V100 has tensor cores that are specifically designed to accelerate tensor operations, making this GPU particularly effective at training neural networks.

5,120 CUDA cores. With 5,120 CUDA cores, the Tesla V100 is highly parallel and efficient for the heavy computations required by deep neural networks.

HBM2 memory. The use of high-speed HBM2 memory enables fast access to large amounts of data, significantly accelerating model training.

NVLink technology. The NVIDIA Tesla V100 supports NVLink technology, allowing multiple GPUs to be connected to a single system with high bandwidth and low latency.

The Tesla V100's high performance enables faster model training times. Tensor cores make it an ideal choice for tensor computations commonly used in deep learning. And the ability to use multiple GPUs with NVLink support provides high scalability when working with large datasets and complex models.

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author: Jennifer
published: 12/20/2023
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