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.
Multi-GPU Scalability
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 A40
The NVIDIA A40 is a powerful computing device specifically built to enhance the processing capabilities of complex visual computing workloads in data centers. It incorporates advanced technologies like the NVIDIA Ampere architecture, featuring RT Cores, Tensor Cores, and CUDA Cores. With 48 GB of graphics memory, the A40 is well-equipped for demanding tasks such as virtual workstations and specialized rendering. By bringing the next-generation NVIDIA RTX technology to the data center, the A40 serves as an optimal solution for advanced professional visualization tasks, providing cutting-edge performance.
NVIDIA A30
The NVIDIA A30 Tensor Core GPU is a versatile compute GPU that utilizes Ampere architecture Tensor Core Technology. It is specifically designed for mainstream enterprise workloads and AI inference, offering support for various math precisions to enhance performance across a wide range of tasks. With its focus on AI inference at scale, the A30 Tensor Core GPU enables rapid re-training of AI models using TF32. Additionally, it provides acceleration for high-performance computing applications through FP64 Tensor Cores.
The A30's compute capabilities are highly valuable due to the combination of third-generation Tensor Cores and MIG (Multi-Instance GPU) technology, which ensures secure quality of service across diverse workloads. This versatility is made possible by the GPU's elastic nature, allowing for efficient utilization within a data center environment.
NVIDIA L4
The integration of the NVIDIA L4 Tensor Core GPU into E2E Cloud's portfolio acknowledges the significance of equipping customers with cutting-edge and energy-efficient hardware. This powerful solution caters to the needs of data scientists, technology professionals, and individuals seeking exceptional performance in their cloud-based workloads.
NVIDIA T4
The NVIDIA T4 GPU possesses exceptional deep learning capabilities. Boasting 16 GB of high-speed GDDR6 memory and 320 Turing Tensor Cores, it delivers outstanding performance in both training and inference tasks for deep neural networks. By harnessing mixed-precision computations and INT8 precision, the T4 GPU achieves accelerated training times and enhanced throughput, resulting in significant speed and efficiency improvements.
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 sales@e2enetworks.com.
