Last updated on January 19th, 2023

Graphics Processing Units (GPUs) are an important component in deep learning as they are designed to perform the complex mathematical calculations required to train deep neural networks. They are able to perform these calculations much faster than traditional Central Processing Units (CPUs), which makes them essential for running large-scale deep learning models.

Deep learning models typically involve a large number of matrix operations, which can be parallelized and executed much faster on a GPU than on a CPU. Additionally, many deep learning frameworks like TensorFlow, PyTorch, and Caffe, have built-in support for GPU acceleration, making it easy for developers to use GPUs for deep learning tasks.

In short, GPUs are essential for deep learning as they provide the necessary computational power and speed to train large deep neural networks, allowing for faster and more accurate results.

In this post, we’ll show you how to find the best cloud GPU for deep learning and machine learning. Let’s get started.

Experience Lightning-fast Computing Power with Cloud-based GPU Resources

Try for FreeChat to Know More

What Are the Types of GPUs?

There are two main types of GPUs available CUDA cores and Tensor cores.

  1. CUDA Cores – CUDA cores are specialized processors designed for general-purpose parallel computing tasks. They are used for a wide range of workloads, including gaming, video editing, and scientific simulations. CUDA cores are available on both Nvidia and AMD GPUs.
  2. Tensor Cores – Tensor cores, on the other hand, are specialized processors designed specifically for deep learning and AI workloads. They are optimized to perform large matrix operations, which are commonly used in deep learning tasks such as image recognition, natural language processing and deep neural networks. Tensor cores are only available on Nvidia GPUs.

The main difference between CUDA cores and Tensor cores is their optimization. CUDA cores are optimized for a wide range of parallel computing tasks, while Tensor cores are specifically designed to accelerate deep learning and AI workloads that involve large matrix operations.

CUDA cores are general-purpose parallel computing processor, while Tensor cores are specialized for deep learning and AI workloads, which are optimized for matrix operations.

Helpful Read: CUDA Cores vs. Tensor Cores – Which One is Right for Machine Learning

Why Should You Use GPU for Deep Learning?

In deep learning implementation, the training phase is the most extensive and resource-heavy phase. Though this phase can quickly be completed in less time if the models have fewer parameters, when this number increases, the time involved in training gradually increases. Thus, resulting in double expenditure.

With GPUs, you can successfully reduce these expenditures, allowing you to work with models with multiple parameters quickly and efficiently. GPUs can deliver such performance because it gives the liberty of conducting the training tasks parallelly and distributing tasks over groups of processors while performing computational operations.

In addition, GPUs are also used for performing computational and target tasks, which is a tough nut to crack for non-specific hardware. Thus, the bottlenecks occurring because of computation limitations no longer exist.

Helpful Read: Why GPUs for Deep Learning? A Complete Explanation

gpu

Factors to Consider to Find The Best GPU for Machine Learning

Here are a few things that you need to consider to find the best GPU cloud server for deep learning:

Licensing

Licensing requirements for different GPUs are also different. For instance, as per the NVIDIA guidelines, some chips are prohibited for use in the data centers. As per the updates of licensing, for the use of consumers, CUDA software has certain limitations. Also, these licensing requirements require transition to the GPUs supported by production.

Interconnection of GPUs

The scalability of any project is highly dependent on the interconnecting GPUs. In addition, the interconnected GPUs decide whether or not more than one GPU and distribution strategies can be utilized. Interconnecting GPUs do not support consumer GPUs. For example, Infiniband connects various GPUs to different servers while NVlink connects multiple GPUs within a single server.

Memory Usage

GPU usage is also affected by the training data memory requirements. To give an example, the algorithms having any medical imagery or long videos as their training data set need GPUs with more memory. In comparison, the basic training data sets will work efficiently with cloud GPUs having less memory.

Machine Learning Libraries

One needs to be aware of the different libraries used by various GPUs, and specific Machine Learning Libraries are supportive of some specific GPUs only. Thus, the choice of GPU highly depends on the Machine Learning Libraries in use. The NVIDIA GPU supports almost all the basic frameworks and MLLs like PyTorch and TensorFlow.

Performance of GPU

The model’s performance is also a factor looked for in selecting a GPU like the basic GPUs are utilized for debugging and development purposes. On the other hand, strong GPUs are utilized to speed up the training time and reduce the number of waiting hours.

Data Parallelism

GPU selection also depends on the size of data being used for processing. If the data set is vast, the GPU should be capable of working on multiple GPU training. In case the data set is more extensive than usual, the GPU should be able to enable distributed training, and it is so because, in this case, servers are required by the data sets for effective and speedy communication.

CUDA cores vs Tensor cores

CUDA cores and Tensor cores are specialized processors designed for different types of workloads. CUDA cores are optimized for general-purpose parallel computing tasks, while Tensor cores are specifically designed for deep learning and AI workloads. When choosing a GPU for AI and deep learning, it is important to consider whether the GPU has Tensor cores, which are specialized for deep learning workloads.

Single precision vs. Double precision

Single precision and double precision refer to the precision of the floating-point calculations that a GPU can perform. Single precision is faster and uses less memory, but it is less accurate than double precision. Double precision is slower and uses more memory, but it is more accurate. When choosing a GPU for deep learning, it is important to consider whether the accuracy of double precision is needed for the specific task.

Power Consumption

Power consumption is another important factor to consider when choosing a GPU for deep learning. GPUs can consume a lot of power, especially when training large deep neural networks. It is important to consider the power consumption of the GPU and how it will impact the overall power usage and cost.

Price

The price of a GPU is also an important factor to consider when choosing a GPU for deep learning. High-end GPUs can be very expensive and may not be necessary for all deep learning tasks. It is important to balance the cost of the GPU with the specific needs of the deep learning task.

When choosing a cloud GPU for deep learning, it is important to consider above-mentioned factors. All these factors will help you to determine the best GPU for the specific deep learning task and budget.

What is the Best Cloud GPU for Deep Learning and ML

Here are a few GPUs that work best for large-scale AI projects:

NVIDIA Tesla V100

NVIDIA Tesla V100 is a GPU that Tensor Core enables for the operations of machine learning, high-performance computing, and deep learning.

The technology used in it is the NVIDIA Volta, which supports the tensor flow technology used for speeding up the deep leaning tensor operations. The Tesla V100 is known for delivering a 4096-bit memory bus, 149 teraflops of performance, and 32 GB of memory.

NVIDIA Tesla K80

The NVIDIA Kepler Architecture forms the base of the Tesla K80, and this GPU is used for speeding the data analytics and scientific computing tasks. This GPU is inclusive of GPU boost and 4,992 NVIDIA CUDA cores.

The Tesla K80 can deliver 480 GB memory bandwidth, 8.73 teraflops of performance, and 24 GB of GDDR5 memory.

NVIDIA Tesla A100

Multi-instance GPU technology and tensor core form up the NVIDIA Tesla A100, which was designed for the operations of HPC, deep learning, and machine learning. This GPU has the scalability of thousands of units and can be divided into 7 GPU instances depending on the workload.

The Tesla A100 is capable of delivering 1,555 GB memory bandwidth, 624 teraflops performance, 600GB/s interconnects, and 40GB memory.

Google TPU

Google’s Tensor Processing Units have slightly different working and purposes. The TPUs are ASICs that are cloud-based and used for deep learning. TPUs are available only on the Google Cloud Platform and used with TensorFlow.

Google TPUs can deliver 128 GB high bandwidth memory and 420 teraflops of performance, and its pod versions have a 2D toroidal mesh network, 100 petaflops of performance, and 32TB HBM.

NVIDIA Tesla P100

NVIDIA Pascal Architecture forms the NVIDIA Tesla P100, designed for deep learning and HPC operations. The NVIDIA Tesla P100 delivers a 4,096-bit memory bus, 21 teraflops of performance, and 16GB of memory.

Try Super-fast, Secure Cloud GPU Today!

How to Optimize GPU Performance for Deep Learning

Here are few tips and tricks to optimize your cloud GPU server for high computing performance.

  • Setting the correct CUDA version: When running deep learning tasks on a GPU, it is important to ensure that the correct version of CUDA is installed and being used. Different versions of CUDA can have different performance characteristics and may not be compatible with certain deep learning frameworks. To ensure optimal GPU performance, it is important to check the version of CUDA being used and make sure it is the correct version for the specific deep learning task.
  • Using GPU-enabled libraries: Many deep learning frameworks, such as TensorFlow and PyTorch, have built-in support for GPU acceleration. When using these frameworks, it is important to use the GPU-enabled version of the library in order to take advantage of the GPU’s computational power. Additionally, there are libraries like CUDA and cuDNN which are specifically optimized for GPU computation.
  • Minimizing data transfer between CPU and GPU: Transferring data between the CPU and GPU can be time-consuming and slow down the training process. To minimize data transfer, it is important to ensure that the data being used is stored on the GPU memory. This can be achieved by using libraries like CuPy that copy the data to the GPU memory before computation.
  • Batch size: The batch size is the number of samples used in one iteration of training. Increasing the batch size can improve the performance of the GPU. It is important to experiment with different batch sizes to find the optimal value for the specific deep learning task.
  • Regularly monitoring GPU utilization: Monitoring the GPU utilization during training can help identify bottlenecks and inefficiencies in the deep learning process. This can be done using tools like nvidia-smi that provide detailed information about GPU utilization, memory usage, and other performance metrics.

Wrapping Up

In conclusion, choosing the best GPU for deep learning is an important step in ensuring that deep learning tasks are performed efficiently and effectively.

The progress of deep learning operations requires high computational power, and when compared with CPUs, GPUs deliver better processing power, parallelism, and memory bandwidth. Hence, GPUs are ideal for machine learning and deep learning tasks.

To find the best GPU for deep learning, it is recommended to consider the specific needs of the deep learning task, such as memory capacity, precision requirements and power consumption. Additionally, it is important to consider the cost of the GPU and to experiment with different GPUs to find the one that offers the best performance for the specific deep learning task.

People Also Reading:

Chat With A Solutions Consultant