As AI technology advances, there's an increasing need for new and improved AI compute power. Explore how computing power is helping push the AI movement forward.
Artificial intelligence (AI) and computing power share a very close relationship. Computing power is essential for AI applications because it helps computer systems process and execute tasks. These applications require substantial computational resources to manage complex algorithms and large data sets, which is where GPUs enter the picture. GPUs, or Graphics Processing Units, were originally designed to accelerate image and video processing but have become essential for managing the intensive data processing and deep learning tasks that AI requires.
In the past few years, we’ve seen AI advancements grow exponentially. Naturally, AI hardware advances need to accommodate this growth and keep up. A study revealed that GPU performance has increased roughly 7,000 times since 2003.
Stronger, faster, more efficient hardware lets researchers and engineers develop increasingly complex AI models. Let's understand how computing infrastructure for AI is evolving to meet the growing demands of artificial intelligence.
The role of GPUs in AI development is undeniable. These powerful processors speed up the complex calculations required for training and deploying AI models. Essentially, they serve as the backbone of modern AI technology. But it’s not just GPUs that are attracting attention.
We are starting to see chips made just for AI that compete with them. These chips are built from scratch to help AI do its job even better and faster. A lot of research and work is being done to improve the future of AI computing. Many companies are investing in AI compute power, which is one reason the global market for AI hardware was valued at $53.71 billion in 2023 and is expected to grow to approximately $473.53 billion by 2033.
Why have AI hardware advances become a topic of conversation recently? The shift towards specialized AI hardware reflects the increasing demands of AI applications across different sectors. To successfully create AI solutions, it is important to stay ahead of the game by being aware of the changes happening to hardware.
Leading hardware manufacturers are racing to develop next-generation hardware, improving performance and efficiency through internal development, strategic partnerships, and acquisitions.
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Apple has moved from using external GPUs to developing its own M-series chips with neural engines for AI acceleration, strengthening its tightly controlled ecosystem. Meanwhile, Google continues to invest heavily in its Tensor Processing Unit (TPU) infrastructure. TPUs are AI chips built to work faster and use less energy than GPUs, which makes them great for training and deploying AI solutions on a larger scale.
Likewise, AMD has entered the AI hardware arena with its Radeon Instinct series of accelerators, targeting data centers and high-performance computing applications. Also, Nvidia continues to focus on developing GPUs optimized for AI workloads, such as the A100 and H100 Tensor Core GPUs. Their recent acquisition of Arm Holdings aims to increase their control over chip architectures that power many mobile devices.
Beyond these established players, many startups and research institutions are venturing into novel AI chip architectures. For instance, Graphcore specializes in sparse computations with its Intelligence Processing Unit (IPU). Cerebras Systems offers the Wafer Scale Engine, a massive chip tailored for extreme-scale AI workloads.
Let’s take a look at the latest AI hardware that’s come out.
On April 9, 2024, Intel unveiled its latest AI chip, the Gaudi 3, boasting superior performance over Nvidia's H100 GPU:
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Prior to Gaudi 3, on March 18, 2024, NVIDIA introduced its latest AI platform, the Blackwell. This platform is designed to power breakthroughs in various fields and has the following features:
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Meanwhile, several tech giants are developing their own custom AI chips to power their services.
On April 10, 2024, Meta announced the latest version of their Meta Training and Inference Accelerator (MTIA). This second-generation chip, already operational in Meta's data centers, is performing better in terms of compute and memory bandwidth. These upgrades support the performance of Meta's AI applications, such as ranking and recommendation engines, on platforms like Facebook and Instagram.
Similarly, other major players like Google, Amazon, and Microsoft have also introduced their custom silicon chips this year. It’s a strategic move to optimize their cost structures and reduce dependency on third-party suppliers like Nvidia.
AI hardware supports various AI solutions across many different industries. In healthcare, it powers medical imaging systems like MRI and CT scans, handling complex tasks and processing large data volumes efficiently for rapid and precise diagnosis.
Financial institutions use AI algorithms to analyze data for fraud detection and investment optimization. The intricate nature of financial data analysis requires advanced hardware capabilities to handle the immense computational workload effectively.
In the automotive industry, it helps process real-time sensor data in autonomous vehicles. Tasks like object detection and collision avoidance need to be backed by advanced hardware with powerful processing capabilities for quick decision-making and passenger safety.
Retailers use AI-driven recommendation engines to personalize shopping experiences and boost sales by analyzing vast customer data across departments to predict preferences and suggest relevant products. The need to analyze diverse datasets and generate personalized recommendations calls for advanced hardware for real-time responses and enhanced user engagement.
Another example related to retail stores is using computer vision to monitor and analyze customer behavior. Retailers can understand how customers interact with their environment, identify popular products, and detect foot traffic patterns. Based on these findings, they can optimize store layouts and product placements to improve sales. Computing power is important for real-time processing of large video data volumes. Accurate tracking of movements and interactions depends on robust hardware. Without it, the speed and accuracy of data processing are compromised, reducing the effectiveness of customer behavior analysis.
That’s the tip of the iceberg. From manufacturing to agriculture, AI hardware can be seen everywhere.
AI hardware is often built to handle big tasks. It can be challenging to grasp the scale of AI deployments in industries worldwide, but it's clear that scalable AI depends on having the right hardware in place.
Take the collaboration between BMW and NVIDIA, for instance. With BMW producing 2.5 million cars annually, the scale of its operations is immense. BMW is using AI to optimize various aspects of its manufacturing process, from quality control and predictive maintenance to logistics and supply chain management.
To meet such demands, BMW relies on advanced AI hardware solutions like NVIDIA's Quadro RTX 8000 and RTX-powered servers. These technologies make AI deployments easier and more scalable.
Beyond providing AI applications with computational power, the AI hardware you choose influences your solution in terms of model performance, model conversion needs, deployment flexibility, and overall accuracy. Once AI models are trained and tested, they are often converted to a format that will run on the chosen deployment platforms.
But, model conversion can lead to accuracy loss and needs to be considered ahead of time. Integration tools such as ONNX (Open Neural Network Exchange) can provide a standardized format for deploying AI models across a diverse array of hardware platforms. This is also the reason behind popular models like YOLOv8 giving users the option to export their custom-trained models in many different formats to cater to multiple deployment options.
The impact of advanced AI compute power isn’t limited to AI; it’s also touching the energy sector.
For example, Meta's LLaMA-3, an advanced large language model (LLM), was trained using two custom-built data center clusters outfitted with 24,576 Nvidia H100 GPUs each. Through this robust hardware setup, Meta was able to increase processing speed and achieve a significant 40% reduction in energy consumption. So, advancements in AI hardware are also contributing to more energy-efficient operations.
Moreover, the connection between AI and energy is getting more attention with people like Sam Altman getting involved. Altman, known as the CEO of OpenAI, has recently made the nuclear energy company Oklo available to the public. Oklo, with its innovative nuclear fission technology, aims to transform energy production, potentially providing power to the data centers essential for AI operations. In the past few years, both Bill Gates, co-founder of Microsoft, and Jeff Bezos, founder of Amazon, have also made investments in nuclear plants.
Looking ahead, the future of AI hardware is set to make huge leaps, especially with the rise of quantum computing. Experts predict that by 2030, the quantum computing market could be worth nearly $65 billion. As AI models grow in complexity, specialized hardware becomes crucial for unlocking their full potential. From AI-specific chips to quantum computing explorations, hardware innovation drives the development of more complex and impactful AI solutions.
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