What Are GPUs?
Graphics processing units (GPUs) are specialized electronic circuits that accelerate image rendering and processing. Initially developed for graphics-intensive tasks, GPUs have evolved significantly. They consist of thousands of smaller cores optimized for parallel processing, allowing them to perform many calculations simultaneously. This architecture is well-suited for operations involving large datasets and complex computational tasks.
In addition to their graphics capabilities, GPUs have become crucial for machine learning and artificial intelligence (AI). Their design allows them to handle tasks that require massive data throughput and high-speed computation efficiently. Compared to central processing units (CPUs), GPUs can offer substantial performance improvements for specific workloads, making them versatile components in both consumer and enterprise technology solutions.
Why GPUs Are Great for AI
Handling Large Matrix Operations
GPUs handle large matrix operations, which are fundamental to AI and machine learning tasks. These operations often involve performing a vast number of calculations simultaneously, a task that GPUs execute efficiently. Traditional CPUs, even very powerful ones, lack the parallel processing infrastructure found in GPUs, resulting in slower performance for these tasks. The ability to process large volumes of data in parallel makes GPUs indispensable in AI applications.
Accelerating Deep Learning Tasks
GPUs accelerate deep learning tasks by providing the computational power to handle deep neural networks, which require intensive computation. These tasks often involve handling complex layers of computations that CPUs would process sequentially over longer periods. The parallel architecture of GPUs allows them to process multiple operations at once, significantly reducing the time required for model training and inference.
Some GPUs have features specific to deep learning, such as tensor cores, which enhance their performance for AI applications. These enhancements focus on increasing throughput for operations vital to deep learning, like matrix multiplications and convolutions. As a result, GPUs have been behind some of the biggest breakthroughs in fields like computer vision and natural language processing.
Performance Improvements in AI Model Training
AI model training benefits markedly from the performance improvements offered by GPUs, especially when dealing with large, complex datasets. The parallel processing capabilities of GPUs allow for faster computation of gradients and parameter updates, which are critical tasks in training neural networks. As a consequence, training times can be reduced significantly, enabling researchers and developers to iterate and refine models more quickly.
Enhanced performance with GPUs also leads to practical benefits such as reduced energy consumption and lower operational costs over time. Faster training phases and more efficient resource utilization mean that fewer computational resources are needed, reducing the workload on hardware and saving time and energy.
Popular GPU Platforms for AI
In order to use GPUs effectively in AI projects, you will need an entire ecosystem, which includes not only the hardware itself but also drivers, supporting software, and networking equipment. NVIDIA, AMD and Intel each provide a platform that offers this ecosystem. In addition, many organizations rely on third party providers hosting pre-configured GPU systems.
NVIDIA
NVIDIA is a leading player in the GPU market and one of the world’s fastest growing companies. Its GPUs are integral to AI applications, notably for their CUDA architecture which allows developers to program in a parallel-computing environment efficiently. This architecture has become a standard in academia and industry, facilitating widespread adoption of NVIDIA GPUs for AI research and development. Their ecosystem includes software, libraries, and development tools that enhance productivity and performance in AI workflows.
NVIDIA’s GPUs are equipped with features specifically for machine learning and deep learning tasks. The introduction of tensor cores in their architectures resulted in significant performance improvements for AI computations, allowing deep learning tasks to execute faster and more efficiently. The company offers products ranging from data center-grade GPUs and massive-scale GPU servers, to lower-cost consumer-grade offerings.
AMD
AMD offers a competitive alternative in the GPU market, focusing on high-performance computing and graphics. Known for their open-source approach, AMD GPUs support various programming models and libraries through their ROCm (Radeon Open Compute) platform. This platform provides an ecosystem that allows AI developers to build, optimize, and deploy machine learning solutions effectively. AMD’s commitment to openness has led to a supportive community and wide-ranging collaborations in AI research and development.
AMD GPUs are increasingly recognized for their performance in AI workloads. They have been optimized to deliver high throughput and efficiency, especially in applications requiring significant parallel processing capabilities. With recent advancements, AMD has closed the gap with competitors regarding performance and power efficiency, making their GPUs a viable option for AI applications seeking alternatives to dominant market players.
Intel
Intel, primarily known for its CPU technology, has also made strategic inroads into the GPU market with its dedicated graphics offerings. Intel’s approach integrates GPUs with their x86 architecture, offering potential advantages in terms of compatibility and performance synergy with their existing CPU technologies. This integration allows for seamless transitions and optimizations in workloads that require both CPU and GPU resources.
Intel’s GPUs leverage the company’s historical strengths in processing power and innovation in chip technology to deliver GPUs capable of handling AI workloads efficiently. Intel’s focus on integrated solutions and its increasing investment into GPU development suggest an expanding role in the AI ecosystem. However, at the time of this writing, Intel is considered a niche player, with its solutions mainly catering to hybrid workloads including both AI and traditional applications.
Cloud-Hosted GPUs
Cloud hosting services offer GPUs as a way to scale AI applications without the need for substantial upfront hardware investments. Specialized hosting providers like Atlantic.net, and general-purpose cloud platforms like Amazon Web Services (AWS), provide cloud-based GPU options that can be tailored to the requirements of specific AI projects. These services allow developers to access powerful GPU technology on a pay-per-use basis, making them an attractive option for businesses looking to manage costs while leveraging high-performance AI capabilities.
Cloud-hosted GPUs provide flexibility and scalability for AI workloads, accommodating fluctuating demand with ease. The ability to spin up virtual machines with GPU acceleration makes it possible to handle peak processing requirements efficiently. Cloud providers also offer support and optimization tools, facilitating the deployment and management of AI models.
Related content: Read our guide to GPU cloud computing (coming soon)
Best GPUs for AI in 2024
NVIDIA A100
Specs:
- CUDA Cores: 6,912
- Tensor Cores: 432
- Memory: 80 GB HBM2
- Mixed-Precision Training: FP16, FP32
- Multi-Instance GPU (MIG) Support
The NVIDIA A100 is a GPU for deep learning and high-performance computing tasks. Built on NVIDIA’s Ampere architecture, it delivers substantial performance improvements, particularly for AI workloads. Equipped with tensor cores, the A100 accelerates both training and inference, significantly reducing the time required for deep learning computations. It supports mixed-precision training, which optimizes performance without sacrificing accuracy.
Its Multi-Instance GPU (MIG) capability allows the A100 to be partitioned into smaller instances. This enables more efficient use of the GPU, allowing multiple tasks to run concurrently, which is particularly beneficial in data center environments. With a memory capacity of up to 80 GB, it can handle massive datasets, making it useful for training complex models.
NVIDIA RTX A6000
Specs:
- CUDA Cores: 10,752
- Tensor Cores: 336
- Memory: 48 GB GDDR6
- Memory Bandwidth: 768 GB/s
- Deep Learning Support: Mixed-Precision
The NVIDIA RTX A6000 is another GPU built on the Ampere architecture, designed for AI and visualization tasks. Its tensor cores enable accelerated deep learning operations, including fast matrix multiplications crucial for AI model training. The RTX A6000 is equipped with 48 GB of GDDR6 memory, providing ample space for handling large datasets and complex models.
Its high number of CUDA cores and AI-specific optimizations allow the RTX A6000 to deliver fast training and inference times. Though it’s a professional-grade GPU, its AI-specific features and memory capacity make it an appropriate option for training neural networks and other AI workloads.
NVIDIA RTX 4090
Features:
- CUDA Cores: 16,384
- Tensor Cores: N/A
- Memory: 24 GB GDDR6X
- Memory Bandwidth: 1 TB/s
The NVIDIA RTX 4090, while primarily designed for gaming, is also capable of performing deep learning tasks. It boasts a high number of CUDA cores (16,384) and a memory bandwidth of 1 TB/s, enabling it to handle data-intensive AI operations efficiently. With 24 GB of GDDR6X memory, the RTX 4090 can manage small to medium-sized deep learning models, making it a feasible option for individual developers or smaller-scale AI applications.
However, it lacks the AI-specific features found in professional GPUs like the A100 and RTX A6000, such as tensor cores optimized for deep learning. While it can be used for AI tasks, it’s less suited for large-scale or highly specialized deep learning workloads compared to NVIDIA’s enterprise GPUs.
NVIDIA L40S GPU
Specs:
- CUDA Cores: Accelerated FP32 throughput for graphics and compute tasks
- Tensor Performance: 1,466 TFLOPS (FP8)
- Single-Precision Performance: 91.6 TFLOPS
- Memory: 48 GB GDDR6
- Max Power Consumption: 350W
The NVIDIA L40S GPU is suitable for AI and data center applications, delivering performance improvements for AI computations and graphics-intensive tasks. Based on the Ada Lovelace architecture, it includes fourth-generation Tensor Cores and a Transformer Engine, which provide enhanced capabilities for deep learning tasks like large language model (LLM) training and inference.
With support for FP8 precision and TF32 computations, the L40S ensures fast processing speeds for AI workloads. The L40S also suits multi-workload environments, handling tasks ranging from generative AI to 3D rendering and video processing. Its 48 GB of memory and NVLink technology enable smooth scaling across multiple GPUs.
NVIDIA H100 NVL
Specs:
- Memory: 94 GB HBM3
- Tensor Cores: Fourth generation, supporting FP8 precision
- NVLink Bandwidth: 600 GB/s
- PCIe Gen5 support
The NVIDIA H100 NVL, part of the Hopper architecture, is purpose-built for large-scale AI training and inference tasks. It delivers 12x higher performance on GPT-3 175B models compared to previous generations. The GPU includes dual GPUs connected via NVLink, offering 188 GB of HBM3 memory, which is essential for massive datasets and training trillion-parameter models.
The Transformer Engine leverages FP8 precision, reducing memory consumption. Additionally, the H100 NVL is optimized for large AI clusters, supporting NVLink and PCIe Gen5, which ensure high-speed communication between GPUs. This scalability is useful for distributed AI workloads, making the H100 NVL suitable for enterprises working on large-scale AI systems.
GPU Features to Consider for AI Workloads
Total Core Count
When selecting a GPU for AI workloads, total core count is a critical factor. A higher number of cores allows for improved parallel processing capabilities, which is essential for efficiently handling the data-intensive calculations involved in AI and machine learning. This translates into faster processing times and the ability to manage larger datasets, enabling more complex models to be developed and refined quickly. A GPU’s core count directly impacts its potential to perform compute-heavy tasks concurrently.
The core count also influences how well a GPU can manage diverse AI applications simultaneously. More cores provide the computational headroom needed for multitasking and executing multiple AI models in parallel. For researchers and developers running extensive AI experiments or production-level AI applications, selecting a GPU with a sufficient core count can significantly improve throughput and reduce time to insight.
Total Memory
Total memory on a GPU determines how much data can be stored and processed simultaneously, directly affecting the ability to handle large datasets and complex models in AI workloads. High memory capacity allows for the storage of entire models and their parameters, facilitating more efficient computation and reducing the need for data swapping between the GPU and system memory. This is especially important in deep learning, where model sizes and data volumes can be substantial.
A GPU with ample memory supports larger batch sizes during training, enhancing the efficiency of the training process. It also enables the execution of resource-intensive tasks without encountering memory bottlenecks, thus speeding up computations and improving model convergence rates.
Memory Clock Speed
Memory clock speed on a GPU determines the rate at which data is transferred from the GPU’s memory to its cores for processing. Higher clock speeds enable faster data retrieval and processing, crucial for tasks that require quick access to large datasets, such as in AI and machine learning applications. A faster memory clock can significantly increase the throughput of data, optimizing the performance of AI workloads by minimizing delays and enhancing computational efficiency.
In scenarios where the GPU frequently alternates between data fetching and processing, having higher memory clock speed can reduce latency and improve overall system responsiveness. This speed is particularly beneficial in real-time AI applications, such as autonomous vehicles and online data analytics, where prompt decision-making is vital.
AI-Specific Hardware Optimizations
AI-specific hardware optimizations in GPUs, such as tensor cores and AI-accelerating algorithms, significantly enhance the performance of machine learning tasks. These optimizations are tailored to the intricate needs of AI workloads, offering capabilities for quicker matrix multiplications and reduced precision computations, which are central to deep learning processes. These dedicated hardware features ensure GPUs efficiently handle the unique complexities involved in training and deploying AI models.
Leveraging AI-specific hardware optimizations allows GPUs to achieve higher performance while maintaining improved power efficiency. This balance is crucial in deploying AI models in power-constrained environments like mobile devices or embedded systems. By utilizing these optimizations, developers can focus on model refinement and application innovation, rather than infrastructure limitations.
GPU Clock Speed
GPU clock speed, measured in MHz, dictates how many cycles per second a GPU can execute, affecting its ability to perform calculations. A higher GPU clock speed generally translates to faster processing of individual tasks, resulting in improved performance for AI applications that rely on quick sequential computations. While AI workloads are typically parallelized, having a high clock speed increases performance for tasks that require intensive processing on a single thread. This means the other factors mentioned above are typically more important for AI workload performance than raw GPU clock speed.
Optimizing GPU clock speed is essential for maximizing performance in AI training and inference. Faster clock speeds lead to quicker execution of operations, thereby reducing overall processing time for computationally heavy models. This speed enhancement is particularly beneficial in scenarios where time-bound solutions are necessary, such as real-time data processing and interactive AI systems.
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Choose from the NVIDIA L40S GPU and NVIDIA H100 NVL to unleash the full potential of your generative artificial intelligence (AI) workloads, train large language models (LLMs), and harness natural language processing (NLP) in real time.
High-performance GPUs are superb at scientific research, 3D graphics and rendering, medical imaging, climate modeling, fraud detection, financial modeling, and advanced video processing.
