Today, with the rapid development of generative AI technology, the commercial effects brought by the application of large models are gradually emerging. Various companies are actively promoting the construction of large model ecosystems in order to occupy a favorable position in this emerging field.

However, when enterprises deploy large models, how can they ensure data security and privacy protection? Considering information security, how to achieve privatized deployment? Faced with the embarrassment of "looking for nails with a hammer", how to find suitable application scenarios? Digital Intelligence How can a company with a weak foundation achieve quick results?



In recent years, a widely discussed topic in the industry is: how to use the shortest path and the lowest cost to achieve verification and exploration of large model implementation. Based on this, the large model all-in-one machine came into being.

What is a large model all-in-one machine?

Unlike Microsoft, Google, openAI, etc., which mainly achieve commercialization through cloud-based AI services and API interfaces, the large model all-in-one machine is a full-stack artificial intelligence device that integrates large model training and inference functions. To put it simply, , which is an integrated device of "hardware + large model supporting software". Its original design is to simplify the deployment and application process of large models, making it easy to use "out of the box". This device usually includes key hardware and software components such as a central processing unit (CPU), graphics processor, memory, operating system, and deep (GPU) learning framework, thus being able to provide a full-stack solution from underlying computing power to application services. 

Generally speaking, the large model training process requires data to be transmitted to a central server or cloud for calculation, and then the results are returned, which involves a large amount of data transmission and network delays. The large model all-in-one machine presets the model in the server, which can realize the privatized deployment of large models and realize multiple functions according to user needs.

Through large-model all-in-one devices, users no longer need to spend a lot of time and energy configuring and optimizing the hardware environment, nor do they need to understand the details of the underlying algorithms and frameworks in depth, nor do they need to worry about the security issues caused by "data on the cloud". This integrated solution The solution shortens the deployment cycle, deeply integrates scenario requirements, and lowers the threshold for implementation, allowing more companies to quickly get started with large models.

It can be seen that compared with ordinary traditional equipment, the advantages of large-model all-in-one machines are very significant. This is also one of the areas that more and more leading manufacturers are keen to deploy in the past two years.


Currently, the application of large models is gradually penetrating into all walks of life, and relevant application cases have been implemented in many fields. Enterprise users‘ understanding of large models is also gradually becoming more rational. From the initial high expectations, they are now paying more attention to the value that large models can play in actual scenarios.

Intelligent manufacturing: The large model all-in-one machine helps enterprises manage data, use models well, and schedule computing power by integrating large industrial models to achieve the optimization and upgrade of intelligent manufacturing.

Energy industry: Kunlun large model has become the first large model to be registered in China‘s energy and chemical industry, promoting the intelligent transformation of the energy industry.

Financial industry: The application of large-scale all-in-one machines in the financial industry, such as intelligent customer service systems and intelligent approval platforms, has greatly improved the efficiency and satisfaction of financial services.

Medical industry: The application of large models in the medical field has greatly improved the level of medical services. For example, the intelligent consultation system based on large models enables rapid preliminary diagnosis and personalized treatment suggestions for patients‘ conditions.

Government services: The application of large model technology has realized the optimization and reshaping of government service processes, and improved the efficiency and satisfaction of government services.

Of course, for now, large-model all-in-one machines have been implemented quickly in fields such as finance, government affairs, and medical care, but relatively slowly in the industrial field. This is because the complexity and particularity of industrial scenarios require that large models must be highly adaptable. Matchability and maturity. Large industrial models need to be able to understand and process professional knowledge and complex processes in the industrial field. This requires large models not only to have strong data processing capabilities, but also to have in-depth industry understanding and logical reasoning capabilities. In the fields of finance, government affairs, medical care, etc., large-scale applications are more concentrated in data analysis, risk assessment, etc., and the technical difficulty and adaptability requirements are relatively low.

In addition, considering the long return period of investment in the industrial sector, companies will be more cautious when investing in new technologies. In fields such as finance and government affairs, large-model all-in-one machines can bring more obvious social benefits and improve public services, so they can be implemented faster.