Understanding NVIDIA GPUs for AI and Deep Learning

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Understanding NVIDIA GPUs for AI and Deep Learning — Photo by Thomas Foster on Unsplash

The evolution of NVIDIA GPUs and their role in AI and deep learning is a story of innovation and adaptation. It began in the mid-20th century with Alan Turing, who posed the profound question: Could machines learn, adapt, and think like humans? His vision for intelligent systems capable of recognizing patterns and solving problems laid the groundwork for artificial intelligence (AI). Over time, this vision matured into machine learning, where algorithms learned to predict outcomes using data, and eventually into deep learning — a powerful subset inspired by the neural networks of the human brain.

The Rise of Artificial Neural Networks

Deep learning introduced artificial neural networks, intricate systems of interconnected nodes designed to emulate human learning. Each node, akin to a neuron, processed data through mathematical operations, adjusting weights to improve accuracy. However, as these networks grew more complex, so did their demand for computational resources. Training deep learning models required processing millions of data points and fine-tuning billions of parameters — a challenge that pushed traditional CPUs to their limits. This computational bottleneck paved the way for the adoption of Graphics Processing Units (GPUs), originally designed for rendering 3D graphics but uniquely suited for the parallel processing needs of AI.

From Graphics to General Computation

GPUs, commonly known as graphics or video cards, were originally designed to compute the brightness of each pixel on a screen. This task is inherently parallel, as each pixel’s computation can occur independently of the others. To accommodate this need for parallel processing, GPUs are equipped with many shader cores, which are specialized units for handling graphics-related computations. Interestingly, the same architecture that makes shader cores efficient for rendering graphics also makes them well-suited for performing the matrix math required in neural network training and inference. By repurposing these cores for AI workloads, GPUs have become a powerful tool for accelerating deep learning tasks.

As screen resolutions increased, the computational demands for rendering graphics also grew, necessitating more shader cores to maintain performance. NVIDIA addressed this challenge with innovations like Deep Learning Super Sampling (DLSS). Instead of relying on brute-force methods to add more cores, DLSS employs a neural network to enhance image quality. The process begins with rendering a lower-resolution image, which is then upscaled to a higher resolution using the neural network. This approach allows GPUs to achieve high-quality visuals efficiently. NVIDIA trained these networks on supercomputers and included them in video drivers, enabling faster and more effective graphics processing.

Tensor Cores and AI Optimization

To further enhance computational efficiency, NVIDIA introduced Tensor Cores — specialized units designed to accelerate deep learning inference. Tensor Cores excel at performing the matrix operations central to neural networks, significantly improving the speed of both training and inference. While initially aimed at improving graphics-related AI tasks, such as DLSS, these cores have broader applications and can accelerate deep learning workloads across various domains. This versatility has solidified GPUs as essential tools not only for graphics but also for advancing AI and deep learning research.

GPUs excel in parallel processing, a feature that aligns perfectly with the matrix math central to neural networks. Unlike CPUs, which have a few highly powerful cores capable of handling tasks one at a time with great precision, GPUs may consist of thousands of less powerful cores that work together on many smaller tasks simultaneously, much like a large assembly line where each worker contributes to completing a single process at high speed. This capability enabled GPUs to handle the heavy computational loads of deep learning with remarkable efficiency. High memory bandwidth further amplified their performance, enabling rapid data transfer between memory and processing units. Over time, GPUs evolved to include specialized components like Tensor Cores and Transformer Engine, optimized to enhance precision and throughput for AI tasks. These innovations made GPUs not only faster but also more versatile, capable of addressing the increasing complexity of AI models.

Scaling AI with GPUs

As AI models became increasingly complex, the need for scalable computational power grew. GPUs addressed this challenge by enabling parallelism across multiple units. NVIDIA emerged as a leader in this domain, introducing innovations such as NVLink and NVSwitch for high-speed GPU interconnects and Multi-Instance GPU (MIG) technology for efficient resource allocation. These advancements allowed GPUs to scale seamlessly from individual workstations to massive data centers capable of training today’s largest AI models. NVIDIA’s DGX systems, featuring arrays of interconnected GPUs, became a cornerstone of modern AI research. These systems demonstrated how integrated hardware and software solutions could tackle the computational demands of cutting-edge research, from natural language processing to complex simulations.

Software Advancements and Ecosystem Integration

Software advancements played a pivotal role in unlocking the potential of GPUs. NVIDIA’s CUDA platform provided a user-friendly framework for programming GPUs, while libraries like cuDNN and cuBLAS optimized operations for deep learning. Frameworks such as TensorFlow and PyTorch further streamlined model development, enabling researchers to focus on innovation rather than implementation. Techniques like mixed precision calculations enhanced computational efficiency by allowing faster processing without compromising accuracy. These tools transformed GPUs into indispensable instruments for AI development and deployment. The seamless integration of hardware and software created an ecosystem where AI innovations could flourish, driving unprecedented advances in the field.

Real-World Impact of GPUs in AI

The impact of GPUs on AI has been profound. Tasks that once required months of computation can now be completed in days or hours. A prime example is AlexNet, a landmark deep learning model. Initially taking weeks to train on CPUs, its training time was reduced to mere days with GPUs. Advances in inference — the application of trained models to new data — have seen dramatic improvements in performance over the past decade. These breakthroughs have democratized AI, making it accessible to researchers and industries worldwide. The acceleration of AI workflows has also enabled real-time applications, such as autonomous vehicles and personalized healthcare, which were previously unimaginable.

Expanding GPU Applications for AI and Deep Learning

Today, GPUs drive a vast range of AI applications, from self-driving cars and facial recognition to personalized medicine and financial modeling. They power generative AI models like ChatGPT, relying on thousands of interconnected NVIDIA GPUs to process and generate human-like responses. GPUs have become central to the AI ecosystem, enabling transformative applications across healthcare, scientific research, and beyond. For instance, in drug discovery, GPUs expedite simulations that predict molecular interactions, while in climate science, they model complex systems to improve weather forecasting.

A History of Architectural Innovation of NVIDIA GPUs

The story of GPUs is not solely about hardware but also about adaptability and evolution. NVIDIA has consistently pushed the boundaries of GPU technology to meet the growing demands of AI and computational workloads. The journey began with NVIDIA’s early recognition of the potential for GPUs to extend beyond graphics rendering and into general-purpose computation. This realization led to the introduction of the CUDA programming platform in 2007, enabling researchers to unlock the parallel processing capabilities of GPUs for scientific and AI applications. CUDA marked the beginning of NVIDIA’s deep commitment to advancing computational hardware.

Subsequent architectures refined these innovations. The Turing architecture built upon earlier breakthroughs by enhancing Tensor Cores introduced by the Volta architecture, which accelerated the matrix math critical to deep learning. Ampere further revolutionized the field by doubling FP32 performance and introducing Sparsity features, which allowed models to be trained more efficiently by focusing computational resources on the most significant data points. These advancements underscored NVIDIA’s focus on creating GPUs that could adapt to increasingly complex AI models and larger datasets.

The recent Ada Lovelace architecture represents a generational leap, delivering dramatic performance improvements over its predecessors. By incorporating lessons from previous architectures, Ada Lovelace optimized power efficiency and computational throughput, enabling AI researchers to tackle even larger and more complex problems. Each new architecture not only builds on past successes but also sets new benchmarks for what GPUs can achieve, continually expanding the boundaries of AI and computational science.

Future Prospects

Looking ahead, the role of GPUs in AI is expected to grow steadily. Developments such as the Transformer Engine introduced by Hopper Architecture, which enhances the efficiency of generative AI models, and ongoing software advancements are likely to improve their utility further. As AI becomes more integrated into various industries and aspects of daily life, the collaboration between GPUs and deep learning technologies will continue to influence technological progress. The ongoing development of GPUs highlights their critical role in providing the computational tools necessary to support the increasing complexity and demands of AI research and applications.

Understanding different types of NVIDIA GPUs for AI and Deep Learning

There are a lot of NVIDIA GPU models which may make it difficult to pick the right model of a cloud GPU for training or inference, to build a GPU for a DIY deep learning workstation or pick the right notebook with a mobile GPU for deep learning and AI. Major cloud providers offer several types of GPUs at a different price per hour and a simple amazon search returns a huge list of items. How to make sense of that and which one to pick?

Market segments of NVIDIA GPUs

The first thing to understand is market segments. NVIDIA offers GPUs for four market segments: consumer, workstation or professional, datacenter and edge GPUs. The examples of the GPUs in each segment are given below.

The names of the GPUs contain certain words that can identify to which segment a GPU belongs. For example, NVIDIA GeForce RTX is not the same as NVIDIA RTX. The former is a less expensive consumer GPU that a gamer or a data scientist will put into their PC and the latter is a more expensive workstation or professional GPU used by researchers and designers. The two GPU types serve different segments in terms of computation power and price. Consumer and Workstation GPUs both have desktop and mobile versions.

The third segment is intended to be used in data centers where several GPUs, e.g eight operate inside servers. The difference of this type of the GPU from the previous ones is that datacenter GPUs may not have graphic output, do not have cooling systems such as fans and rely on external cooling, and have lower power consumption. They are designed for power and space efficiency as data centers may operate tens of thousands of GPUs.

The fourth segment, Jetson, is intended for robots and drones. These GPUs are optimized for inference and are usually less powerful than the GPUs in other segments. These GPUs can be used for various Deep Learning experiments but with small neural networks.

One can find cloud GPUs of the first three segments. Laptops usually contain mobile versions of only consumer or workstation GPUs. GPUs belonging to all three segments can be used to build a DIY AI or Deep Learning workstation, although some server GPUs cannot be inserted into a PCI slot, they have a different socket. Also, one needs to think about cooling for data center types of GPUs and have an additional GPU for video output.

While GPU segments stayed the same, their names have changed. The previous name of the workstation segment was Quadro and datacenter segment — Tesla. If you see these words on a GPU or its packaging, this means that it is an older GPU. However, on shopping sites the old names can be used as synonyms for the respective segments and one can check the name of the architecture in the GPU description, to tell whether it’s a newer or older version. The architecture is also indicated by a letter in the names of some GPUs such as P100, V100 or A100, where P means Pascal, V stands for Volta and A for Ampere; GPUs with Ada Lovelace architecture do not have a letter.

Architecture of NVIDIA GPUs

From time to time NVIDIA releases a new design for GPUs in the first three segments called architecture. Only some architectures have Jetson GPU models. The GPUs of a newer architecture offer some new features and can be more powerful than older ones. While GPUs of all segments having the same architecture share a lot of similarities, there are differences that put them in different segments. In addition, there are usually several GPUs with the same architecture within each segment that have different performance and price.

Possible confusion with GPU naming

As seen from the table above, the GPUs of the workstation segment with Ada Lovelace architecture do not have a letter designating the architecture. The architecture of other other GPUs of workstations and data center segments can be inferred from their names. There is sometimes some degree of confusion in the naming, such as the more recent RTX 6000 having Ada Lovelace architecture can be confused with Quadro RTX 6000 having older Turing architecture. Check the images of the GPU for the word Quadro and architecture in the description.

I’ve shared my experience of building of AI Deep Learning workstation in another article.

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