With the dawn of the AIGC era represented by large models, the demand for computing power continues to explode, and the deep integration of AI and edge computing has become an inevitable trend. More and more enterprises are actively deploying GenAI.

The commercial deployment and application of GenAI technology have become a new frontier for enterprises to compete, outlining a new ecosystem where large models shift from "technical power" to "productivity".

Computing power is productivity, and richer computing resources have become the core cornerstone of AI competition.

IDC predicts that the global AI computing market will grow from $19.5 billion in 2022 to $34.66 billion in 2026, of which the GenAI computing market will grow from $820 million in 2022 to $10.99 billion in 2026.

The proportion of GenAI computing in the overall AI computing market will increase from 4.2% to 31.7%.

The Rise of Edge Computing Amid AI Computing Power Bottlenecks

With the explosion of AI large models, the computing power required for model iteration and training has grown exponentially. At the same time, the scale of individual AI supercomputers is also constrained by factors such as power consumption, land, and heat dissipation, and the gap between computing power supply and demand continues to widen.

Previously, the ChatGPT official website temporarily stopped the purchase of Plus paid projects, and after the launch of GPT-4, it has repeatedly reduced access restrictions for paying users, due to the surge in traffic exceeding the server's capacity.

As the GenAI boom gradually expands, the shortage of computing power has become a common challenge facing the industry.

If traditional computing power is the skeleton of AI large models, then edge computing power is the nervous system that pervades the entire body.

NVIDIA points out that in order to effectively utilize computing power to achieve AI application goals, large-scale data centers will inevitably increase capital expenditure to expand cloud computing performance, which will also drive sales of edge devices.

In the process of AI being implemented in actual scenarios, the importance of edge computing power will accelerate its prominence. Future AI computing will exhibit a pattern change of "training and iteration in the cloud, with gradient distribution of inference and content production (cloud side + fog side + edge side)", and edge computing power is expected to become an important component of AI computing power.

Compared to traditional cloud computing, edge computing, as a distributed computing architecture, offers advantages such as low latency, high security, high reliability, and protection of user privacy. It is crucial in real-time decision-making in various fields such as autonomous driving, healthcare, finance, and manufacturing.

From an efficiency perspective, edge computing enables GenAI models to process data at the edge, significantly reducing latency and enabling faster insights. This means that critical decisions can be made in real-time, improving operational efficiency, enhancing customer experience, and achieving better overall business outcomes.

Secondly, by utilizing edge computing, enterprises can distribute computing loads across the edge device network, optimizing resource utilization and enabling effective scaling. This approach minimizes the pressure on centralized cloud infrastructure and optimizes bandwidth usage, resulting in cost savings and improved performance.

Thirdly, with edge computing, GenAI models can run directly on edge devices or local servers, minimizing the need to transmit sensitive data to centralized cloud servers.

By bringing data closer to its source, companies can significantly reduce risks associated with data breaches, unauthorized access, and compliance issues.

In terms of relationships, edge computing does not seek to replace cloud computing but rather serves as an important complement. The collaboration between edge computing and the cloud enables a hybrid architecture that maximizes the advantages of both paradigms.

GenAI models can leverage the scalability and storage capabilities of the cloud while benefiting from the low latency and local processing capabilities of edge devices. This integration ensures a versatile and adaptable infrastructure for GenAI adoption.

Edge Computing Redefines the Boundaries of Productivity

As enterprises increasingly embrace AI, the integration of edge computing and AI holds tremendous potential to transform global industries. By leveraging localized processing, real-time insights, and optimized resource utilization, the full potential of AI can be unleashed while protecting sensitive data and propelling organizations into the AI era.

In response, global technology giants are increasingly turning their attention to this area.

For example, Huawei and Qualcomm have both launched edge AI products. Last March, Qualcomm China demonstrated Stable Diffusion with over 1 billion model parameters for the first time on Android phones; Huawei released a smart image search function in July last year, which utilizes model miniaturization.

Huawei and Qualcomm have to some extent validated the feasibility of high-performance edge AI and demonstrated that the combination of model compression and networked intelligence is expected to enable the experience of large AI models at the edge.

Furthermore, NVIDIA's Jensen Huang has stated that the next wave of artificial intelligence will be embodied intelligence. Embodied intelligence enables manipulation and perception in the physical world, outputting various mechanical actions.

Embodied intelligence elevates the demand for edge computing power to a new level. The "brain" of embodied intelligence not only needs to process visual information and generate prompts but also needs to be responsible for outputting instructions to execute mechanical actions. In scenarios where mobile chips cannot meet the required computing power, edge IDCs will be an effective supplement.

The emergence of new AI applications such as AIPC, AI phones, embodied intelligence, and autonomous driving has undoubtedly had a significant impact on the edge cloud market, reflected both in the expansion of market size and in the increase in technical requirements.

AIPC and AI phones, as terminals that benefit everyone, essentially rely on a hybrid collaboration between the cloud and the local end. They leverage the cloud's big data processing capabilities to enrich the usage scenarios of local devices.

This hybrid collaboration model places higher demands on the performance and stability of cloud computing, while also generating more data processing and storage needs for cloud computing.

As the data processing center closest to users, edge clouds can rapidly respond to these demands, providing low-latency, high-bandwidth data processing services.

The development of embodied intelligence and autonomous driving has further propelled the rapid growth of the edge cloud market. Intelligent robots need to perform various tasks in real physical environments, requiring edge clouds to provide powerful real-time computing capabilities and data interaction capabilities.

Moreover, edge AI application scenarios continue to enrich. Zenlayer, a cloud service provider specializing in edge computing, states that for applications with extremely high real-time requirements, such as autonomous driving and intelligent manufacturing, edge computing can provide millisecond-level low-latency responses.

By deploying edge computing nodes on vehicles or production lines, customers can process sensor data in real-time, make decisions and control, thus ensuring safe and efficient production operations.

Secondly, for applications that need to process large amounts of data, such as intelligent video surveillance and smart cities, edge computing can alleviate the pressure on central clouds and enable localized data processing.

By deploying edge computing devices near cameras or sensors, video streams can be analyzed in real-time, abnormal events can be identified, and timely responses can be made.

In addition, edge computing can also address issues related to data transmission and privacy protection in large models. By placing the model inference process at the edge, the amount of data transmitted can be reduced, network bandwidth requirements can be lowered, and the privacy and security of user data can be protected.

In fact, some enterprises have already begun to explore the application of edge computing in AI large models.

According to Zenlayer's technical experts, a startup focused on large model technology has built an efficient and stable data transmission channel based on Zenlayer's comprehensive SDN solution, utilizing techniques such as intelligent routing and data transmission protocol optimization. This ensures timely and accurate data support for critical stages such as model training and inference, significantly improving model application effects and user experience.

Furthermore, for some AI large model customers who wish to deploy computing resources overseas, Zenlayer provides computing power hosting or leasing services, deploying computing resources in edge data centers close to users. This allows large model inference to be performed locally, significantly reducing data transmission latency and costs.

Simultaneously, Zenlayer also provides customers with a series of data local storage and transmission solutions, optimizing business interaction experiences while satisfying various countries' data compliance requirements.

Conclusion

Looking further ahead, edge computing is always present behind disruptive technologies such as AI. As the world is boiling in the AIGC trend, edge cloud service providers are meeting the diverse AI application scenarios through the integration and collaboration of edge and cloud computing, enabling AI technology to benefit everyone with more flexibility and computing power.

Related Readings

Edge Computing Enters the "Golden Age"

The Edge Cloud Race Begins: Who Will Be the First to "Advance"?

Will AI Large Models Spark a New Round of AIGC Arms Race?

Cloud Computing Vendors Face an Upgrade Inflection Point, and Edge Computing Layout May Become the Decisive Point

【Original Report by TechCloud】

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