Artificial intelligence (AI) has become an integral part of our lives, much like electricity spread over 100 years ago, revolutionizing industries and changing our lives. Today, artificial intelligence is everywhere, from industrial automation to smartphones. Operating silently behind the scenes, artificial intelligence guides our decisions and significantly influences our choices. It leads us through video recommendations and algorithms, permeating our daily lives with its crucial role in drug discovery, medical diagnosis, and scientific breakthroughs.
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What are AI factories?
With the advancement of technology, there is an increasing direct interaction between humans and artificial intelligence, and its products such as ChatGPT and Gemini. To meet this growing demand for interaction with artificial intelligence, we need sophisticated computer centers equipped to meet the increasing needs of individuals and industries.
Just as data centers involve a wide network of servers, hardware, and cables in a secure environment, artificial intelligence factories feature a similar infrastructure with a focus on producing valuable codes and analytics. Although they operate on the same basic principle, they are preferably not referred to as data centers but rather as factories where data and electricity enter and valuable codes and analytics emerge.
The value of these factories lies in their ability to produce diverse outputs, ranging from human-like texts to images and videos, which can be crucial factors in the development of artificial intelligence technology and practical applications in various fields. Additionally, these codes may include commands to control various devices such as robots and self-driving cars, along with valuable analytics that contribute to improving processes and decision-making in various domains.
Exploring artificial intelligence factories: technologies and applications in the industry of the future
Artificial Intelligence Factories: Data Centers of a New Kind
Extensive networks of servers, secure environment, efficient cooling system... These traits may sound familiar, right? They are definitely the characteristics of data centers. But what if I told you that these characteristics also apply to a new type of facility, AI factories?
- Not just data centers: Nvidia CEO Johnson Huang believes that AI factories differ from data centers in their function and core components.
- Converting raw materials into finished products: AI factories are similar to traditional factories in converting raw materials (data and electricity) into valuable end products (tokens).
- End Products: Symbols of value: The symbols may seem intangible, but they are of great value, and form various outputs such as text, images, videos, and commands to control robots and self-driving cars.
- Improve work and decision-making: Codes can also be used to generate valuable insights and analytics that improve work and decision-making across the board.
These AI factories specialize in training and running large AI models quickly and efficiently, making AI processing units, such as the BlackWhale B200 chipset, an essential and vital part of their servers.
The work of these factories:
1. Training artificial intelligence models
This process involves data preparation and model architecture design, where processed data is fed into the artificial intelligence model for training. This process is repeated to enhance the model and increase its accuracy, and when the model reaches the desired performance, it is deployed for task execution.
This process requires high computational power and significant time, but artificial intelligence factories aim to improve these aspects. Nvidia has made significant improvements in training efficiency thanks to its Blackwell graphics processing units. In the past, training a model with 1.8 trillion parameters required 8000 graphics processing units and consumed 15 megawatts of power. However, the new Blackwell architecture achieves the same results with fewer graphics processing units (only 2000 units) and consumes 73% less power (4 megawatts).
2. Running artificial intelligence models
Running artificial intelligence models involves loading a trained model and using it to process input data to produce the desired results. This process is known as inference and typically requires significant computational power, especially with large models like Gemini and ChatGPT.
Advanced graphics processing units (GPUs), such as those offered by Nvidia, can facilitate the inference process due to their enhanced power compared to traditional processors. When models are used by millions of users, large distributed platforms can distribute the workload, enabling effective scalability to handle millions of inference requests simultaneously. Nvidia claims significant improvements in inference capabilities, stating that it has improved the performance of the 3-GPT model by 7 times using the GB200 graphics card instead of the H100.
Who makes artificial intelligence factories?
Artificial Intelligence Factories, as envisioned by Nvidia, have not yet been established. However, by the end of 2023, Nvidia announced a collaboration with Foxconn to develop these factories, likely utilizing graphics processing units and software from Nvidia.
In March 2024, Nvidia's CEO revealed a digital twin of an artificial intelligence factory equipped with 32,000 Blackwell B200 graphics processing units. This factory was specifically developed to serve Amazon Web Services (AWS), indicating it could be the first artificial intelligence factory.
Nvidia currently leads as the primary supplier of artificial intelligence factories, but it's likely that companies like AMD and Intel will begin developing their own solutions, despite their current focus on developing graphics processing units to compete with Nvidia's products.
What is the future of artificial intelligence factories?
Artificial Intelligence Factories have the potential to change the rules of the game and enhance innovation across various industries, boosting work efficiency and productivity, and reshaping the way we work and live. Therefore, more of these factories are expected to be seen in the near future, with other companies likely to join this sector alongside Nvidia.
The primary focus will be on increasing computational power and improving efficiency, especially in terms of performance and energy savings. This is likely to involve enhancing the performance of Graphics Processing Units (GPUs) and improving networking devices to accelerate data transfer between them.
The development of Artificial Intelligence Factories relies on their users, as researchers and developers of AI models depend on them to build larger and more complex models to tackle complex tasks in various industries. Consequently, AI Factories facilitate the continuous development of advanced AI models, and vice versa, as these advanced models require more efficient AI Factories.
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