The popularity of ChatGPT has attracted global attention, with numerous tech companies and teams joining the hotspot. ChatGPT-like applications rapidly gain widespread adoption and the demand for computing power keeps rising.
On April 15th, Sam Altman, OpenAI’s CEO, gave a respond to a recent open letter about pausing development of GPT-5 by Elon Musk and others. Sam said, “The letter says that OpenAI is training GPT-5. We are not, and won’t for some time. We are doing other things on top of GPT-4 that I think have all sorts of safety issues that are important to address and were totally left out of the letter”
What Sam Altman calls “other things” include website crashes due to surging visits and multiple access restrictions for paying users since GPT-4’s launch. This indicates that currently, there is an imbalance between supply and demand in computing power level of the AI model, and the shortage of computing power has slowed down the pace of industry participants.
To alleviate the pressure caused by the power shortage, organizations are becoming more focused on cloud-native when deploying, and the use of cloud computing is showing explosive growth. As hyperscale cloud service providers continue to introduce innovative solutions to extend its functions closer to the edge, the adoption of edge computing is getting more attraction.
According to OpenAI’s research, the current growth rate of computing power required for AI training has exceeded the growth rate under Moore’s Law of hardware. Moore’s Law states that computing power doubles in every 18-24 months, while the computing power of AI shows an exponential growth. After some targeted optimizations, the reasoning process of AI in the future will be fully capable of being implemented through the power of edge computing.
Gartner, an authoritative consulting firm, has recently released a report named “Competitive Landscape: Hyperscale Edge Solution Providers”. It also made a bold prediction: “By 2025, over 70% of organizations will deploy Hyperscale cloud edge resolution cases for at least one of its edge computing system and will combine it with their cloud deployments. This is much higher than the less than 15% in 2022.”
This demonstrates that edge computing is constantly becoming a crucial component of AI computing power and may even emerge as the core driving force. This is actually related to the relationship between cloud computing and edge computing.
“The octopus” with large heads and many tentacles is usually used by the insiders to describe the relationship between the two. The octopus has a unique physiological structure with many neurons, but only 40% of them are in the brain, while as many as 60% of the rest are scattered across its eight tentacles. When encountered problems, the octopus can “nearby” use its tentacle instead of its brain to “think”, which makes it extremely responsive when hunting.
Edge computing, on the edge side of the network, close to the object or data source, is a distributed and open platform that integrates core capabilities such as networking, computing, storage, and applications. It provides edge intelligence services nearby to meet the key industry requirements for digital transformation in agile connections, real-time business, data optimization, application intelligence, and security and privacy protection.
In the development and application of technology, edge computing and cloud computing complement each other, forming an indispensable new piece of the cloud computing ecosystem after the launch of public cloud, customized cloud and private cloud. As a vital supplement to human digital life, edge computing provides more extensive application and value for people’s life.
To date, with its application scenarios becoming increasingly clearer,edge computing has made significant breakthroughs in various aspects of human life. Companies with edge computing strength are emerging and actively developing it to help accelerate the AI industry, IoT, and 5G communications into the intelligent era.
More and more AI products have begun to take advantage of edge computing power in their design. Companies like Stable Diffusion, Midjourney, JUNLALA, and Adobe Firefly are exploring innovative commercial solutions that combine AI with edge computing power. JUNLALA’s technical team believes that an outstanding AI product cannot do without the strong support of edge computing, and the potential of edge computing is quietly accumulating, enabling us to realize the vision of ubiquitous computing power.
As a rising star in Silicon Valley’s AI circle, JUNLALA adheres to the concept of being “innovative, energetic, capable and steadfast”. It is committed to promoting the development and application of artificial intelligence technology, providing excellent AI products and services to customers worldwide.
In the seven years of hard work, they continue to accumulate experience, constantly improve themselves, and perfect their machine learning algorithms and deep neural networks. They have also advanced natural language processing and image recognition technology.
In the emerging technology revolution driven by generative AI, the explosive growth of edge data communication, storage, and processing volume offers a promising outlook. In the future, the value of edge computing will be released by more pioneering companies like JUNLALA, providing customers in various industries with strong support for digital and intelligent transformation and upgrading.