Deep learning is a field with extraordinary computational requirements, so your choice of GPU will inherently determine your experience with deep learning. What features are essential if you buy a new GPU? Is it GPU RAM, cores, tensor cores, caches? How do you make a cost-efficient choice? This blog post will examine these questions, tackle misconceptions, and help you acquire an intuitive understanding of how to think about GPUs.
But first, what do GPUs do? GPUs provide the computational power needed for efficient training and inference processes. While GPUs were initially designed for rendering graphics in gaming and visualization applications, their parallel architecture and ability to perform complex mathematical calculations have made them well-suited for accelerating machine learning tasks.
Why GPUs for Model Training?
The most resource intensive phase of model development is the training phase. It involves intensive computing on large datasets that may take days to run on a single processor. This process can be completed in shorter time periods if the number of parameters is low, but as the number increases, the training time also increases.
Graphics Processing Units (GPUs) can greatly accelerate the training process for many Deep Learning models. Training models for tasks like image classification, video analysis, and Natural Language Processing involves compute-intensive matrix multiplication and other operations that can take advantage of a GPU's massively parallel architecture. However, if you can transfer these tasks to GPUs, the training time will be reduced significantly.
GPUs enable you to run models with a huge number of parameters efficiently and in shorter times. This is because GPUs can run several training tasks in parallel. They are also optimized to perform target tasks, finishing computations faster than non-specialized hardware. These processors enable you to process the same tasks faster and free your CPUs for other tasks.
Factors to Consider While Choosing a GPU
Choosing the right GPU for AI workloads is crucial to ensure optimal performance and efficiency. As AI tasks involve complex computations and large datasets, selecting a GPU that can handle these requirements is important. Several factors have to be considered when making this decision. By carefully evaluating these factors, you can make an informed choice and select a GPU that best suits your AI needs.
CUDA Cores and Architecture
CUDA (Compute Unified Device Architecture) cores are the processing units in NVIDIA GPUs that are specifically designed for parallel computing. More CUDA cores generally lead to better performance for AI tasks. Additionally, consider the GPU architecture, as newer architectures often offer improved performance and efficiency.
Memory Capacity and Bandwidth
AI workloads often require large amounts of memory to handle extensive datasets and complex models. Ensure that the GPU has sufficient memory capacity (VRAM). Additionally, pay attention to memory bandwidth, as it affects the speed at which data can be transferred between the GPU and its memory.
If you anticipate running large-scale AI workloads or training complex models, check if the GPU supports multi-GPU configurations, such as SLI (Scalable Link Interface) or NVLink. This allows multiple GPUs to work together, providing increased processing power.
Price and Budget
GPUs vary in price depending on their performance and capabilities. Consider your budget and the cost-effectiveness of the GPU in relation to your specific AI requirements.
Best GPUs for AI Model Training
As the demand for efficient and powerful GPUs continues to rise, it's crucial to identify the top performers that can accelerate Machine Learning workflows effectively. It is important to remember that each use case may have different requirements, which is why it is important to consider all specifications. Here is a list of 7 GPUs that can work well for your AI training workload. By understanding their specifications and features, you can make informed decisions when choosing the right GPU for your Machine Learning projects.
NVIDIA Tesla A100
The A100 GPU has multi-instance GPU technology and can be partitioned into 7 GPU instances for any size workload. It can be scaled up to thousands of units and was designed for Machine Learning, data analytics, and HPC. The NVIDIA Tesla A100 is built on the Ampere architecture and features 6,912 CUDA cores. Each Tesla A100 provides up to 624 teraflops performance, 80GB memory, 1,935 GB/s memory bandwidth, and 600GB/s interconnects. The NVIDIA A100 GPU is widely adopted in various industries and research fields, where it excels at demanding AI training workloads, such as training large-scale deep neural networks for image recognition, natural language processing, and other AI applications.
NVIDIA Tesla V100
The V100 is built on the NVIDIA Volta architecture, which introduces advancements in GPU architecture, including the use of Tensor Cores and improved CUDA cores for accelerated computing. It comes in 16 and 32GB configurations, and offers the performance of up to a 100 CPUs in a single GPU.
It has 640 Tensor Cores and is the first GPU to break the 100 TFLOPS barrier. The NVIDIA NVLink connects several V100 GPUs to create powerful computing servers. In this way, AI models that would consume weeks of computing resources on previous systems can now be trained in a few days.
NVIDIA Quadro RTX 8000
Equipped with 48GB of high-speed GDDR6 memory, the Quadro RTX 8000 provides ample memory capacity for processing large datasets and training complex deep learning models. The large memory capacity allows for handling memory-intensive AI workloads, enabling efficient processing of vast amounts of data during training. It also features 4,608 CUDA cores, 576 Tensor Cores, 72 RT Cores, delivering excellent parallel processing capabilities, and enabling fast computation for AI training tasks.
The Quadro RTX 8000 also supports real-time ray tracing, a rendering technique that produces realistic lighting and reflections in graphics. This feature is particularly useful in AI applications that involve computer vision, rendering, and simulation, allowing for more accurate visualizations and improved accuracy in AI training.
AMD Radeon VII
The Radeon VII features 3840 stream processors, providing substantial parallel processing power for demanding tasks such as AI training. With 16GB of high-bandwidth memory (HBM2), it offers ample memory capacity to handle large datasets and complex AI models effectively. When it comes to AI training, the Radeon VII is capable of delivering strong performance. It supports OpenCL and AMD's ROCm (Radeon Open Compute) framework, allowing users to leverage popular AI frameworks like TensorFlow and PyTorch for their training workloads.
The NVIDIA K80 is a dual-GPU accelerator card designed for a wide range of compute-intensive workloads, including AI training. Although it is an older generation GPU, it still offers significant computational power and memory capacity. One notable feature of the K80 is its support for NVIDIA GPU Boost technology, which dynamically adjusts GPU clocks to maximize performance based on the workload's power and thermal limits. This feature ensures optimal performance and efficient power usage during AI training tasks.
NVIDIA Tesla P100
The NVIDIA Tesla P100 is a GPU specifically designed for AI training tasks. It is built on NVIDIA's Pascal architecture and has 3,584 CUDA cores, providing exceptional parallel processing capabilities. It also has 16 gigabytes (GB) of High Bandwidth Memory 2 (HBM2), which offers faster data transfer rates as compared to traditional GDDR5 memory. This high memory capacity and bandwidth enable efficient handling of large datasets during AI training, enhancing overall performance. It supports NVIDIA's NVLink technology, which enables high-speed communication between multiple GPUs, allowing for scalable and efficient multi-GPU configurations. This is particularly useful for training deep neural networks that require extensive computational resources.
NVIDIA RTX Titan
The NVIDIA Titan RTX offers powerful specifications that make it a viable option for AI workloads. The Titan RTX has 4,608 CUDA cores, providing significant parallel processing power for AI calculations. It comes with 24 GB of GDDR6 memory, which offers ample capacity for handling large datasets and complex models during training. The GPU also includes 576 Tensor cores, allowing efficient matrix operations for deep learning tasks. The Titan RTX supports real-time ray tracing and DLSS, enhancing its performance for AI applications that involve complex visual rendering and image processing.
Launching a GPU on E2E Cloud
To launch a GPU using E2E Cloud’s MyAccount, you can sign in to your MyAccount portal on the E2E Networks website. After logging in, you will see the following screen.
Click on Compute and select GPU from the dropdown menu.
You will see the following screen when you choose GPU, with the options E2E Cloud offers for you AI/ML workloads.
Click on Create to launch the machine of your choice.
You can choose the pricing option as per your requirement.
Fill in all the details and launch the machine.
Launching a GPU on E2E Cloud’s MyAccount is a simple process. In case of any further help, feel free to get in touch with firstname.lastname@example.org.