Cressy from the temple of Wafei
Quantum Position | 公众号 QbitAI
The war of 100 models is intensifying, and major manufacturers have rolled out different forms:
Some flex their muscles and ride the dust in the length of text, while others are deeply integrated with search and other functions to become all-round AI assistants...... The dazzling array of large-scale model products is dizzying.
However, for enterprise users, although these general-purpose large models have their own characteristics, they are not so perfect when it comes to solving industry tasks.
The reason for its "incompetence" is mainly that the knowledge reserve of the general model in a specific industry is not sufficient, and there are even serious hallucinations.
In order to make up for such shortcomings, a large amount of industry data is needed, and model algorithms also need to be optimized, which will involve computing power problems......
It is also in this context that in order to help enterprise users better realize the application of large models, Inspur Information has proposed an end-to-end solution integrating algorithms, computing power, data and interconnection.
Large models have entered the 2.0 era, but it is still difficult to land
With the rapid development of AI technology, AI is becoming ubiquitous, affecting a variety of computing devices and platforms.
From servers to personal computers (PCs) and even mobile devices, AI computing power is infiltrating every computing device, and the computing power paradigm for artificial intelligence is constantly innovating.
In addition to this background of "all computing is AI", the representative product of artificial intelligence, the large model, has also entered the 2.0 era.
This means that there will be larger models, and with them more data requirements, as well as greater demands on computing resources.
Algorithms, computing power and data are the "troika" of artificial intelligence development, but from the perspective of today's development, they are not fully developed.
First of all, due to the extremely high training cost of large models, the cost of trial and error is extremely high, and then there is a situation that the innovation of algorithms is still conservative in the era of large models.
Lin Yonghua, vice president and chief engineer of Beijing Zhiyuan Research Institute, believes that in the era of large model 2.0, we should not only focus on the capabilities of a single chip, but also need to consider the storage and computing relationship from the chip to the server cluster to the data center, as well as the coordination of the entire network.
The same is true for data, as the demand for computational scale for large models increases, the known data generated by humans is about to or even insufficient for large models.
In addition to the shackles of computing power and data in the development of large models themselves, there are still more practical problems for enterprise users.
First, the large model lacks professional industry data, which inevitably leads to the problem of illusion and is difficult to apply to enterprise scenarios.
On the other hand, in some enterprise application scenarios, the model window length is extremely demanding, and the existing model may not be able to meet the requirements.
In addition, the difficulty of development and the high technical threshold are also an important barrier for enterprises to realize the implementation of large models.
In order to solve these pain points and difficulties, the entire ecology from top to bottom needs to make efforts to do so, and Inspur Information is one of the boosters for the implementation of the enterprise model.
End-to-end development of enterprise large model applications
At the 10th IPF Inspur Information Ecological Partner Conference, Wu Shaohua, Director of AI Software R&D of Inspur Information, grandly released the enterprise large model development platform Yuannao Qizhi EPAI.
The EPAI platform provides an end-to-end solution for the implementation of enterprise large models, which solves the difficulties of complex enterprise large model application development process and high thresholds.
In the era of large model 2.0, data is an asset, and mastering data is equivalent to mastering the right to speak.
EPAI provides hundreds of millions of pieces of basic data, and also includes automated data processing tools, which can help users organize industry data and professional data, generate high-quality fine-tuned data and industry/enterprise knowledge bases, and then create enterprise-specific data assets.
With the support of high-quality foundation + industry + enterprise data, the accuracy and reliability of the content generated by the large model will be guaranteed, and the illusion problem will be greatly reduced.
At the same time, combined with the Retrieval Enhanced Generation (RAG) technology, EPAI can solve the contradiction that the enterprise knowledge base is updated frequently but the fine-tuning of large models is time-consuming and infrequently, ensuring that the model can obtain the ability to process the latest knowledge in a timely manner.
On the other hand, EPAI also provides efficient fine-tuning tools to support the rapid re-learning of hundreds of billions of parameter models for industrial knowledge, and allows the model to have the ability to process long documents with millions of tokens, solve the problem of insufficient window length, and quickly build large domain models.
EPAI not only has a powerful enterprise model development function, but also helps developers lower the threshold for use in terms of ease of use.
In terms of usage, EPAI supports API, Dialogue UI, and Agent, which can meet the needs of different business scenarios, and allows developers of different skill levels to have development methods that match their capabilities.
Even non-professional developers can quickly learn how to use the platform after a few days of training, free from the limitations of professional knowledge. EPAI has realized the popularization of large-scale model application development and reduced the labor cost of enterprises.
For example, Wu Shaohua said that if you want to develop an "intelligent programming assistant", even a very experienced engineer may need two to three weeks, but with EPAI, it can be executed very quickly.
In addition, EPAI not only supports multiple computing power including CPUs and various GPUs, but also supports self-developed "source" large models and other mainstream open source and closed-source models, which is fast to adapt and low migration costs, providing enterprises with a wealth of models and computing power options.
In addition, EPAI also provides a solid guarantee for the data security that enterprise users are most worried about, ensuring the security of data and models through various technical means such as permission management, data encryption, and content review, so that private information is not leaked.
Compared with other development platforms, one of the unique advantages of Inspur Information is that it is a partner in the Inspur information ecology from the "left-handed" underlying technology providers such as computing power and models to the "right-handed" software developers in various industries.
This means that the EPAI platform can be connected to computing power and applications, becoming a "transportation hub" for the entire ecosystem, accelerating rapid application-oriented innovation.
EPAI solves the problem of algorithm and data well for enterprise users, but the support provided by Inspur Information is not limited to this, but a comprehensive layout integrating algorithms, computing power, data, storage and interconnection.
In terms of computing power, for more and more large model inference scenarios, Inspur Information also cooperated with Intel to release an AI general server that can run 100 billion parameter large models, in terms of storage, released a distributed all-flash storage AS13000G7 to solve the challenge of large model training data, and in terms of interconnection, released the first super AI Ethernet switch X400 in China to accelerate large model training and inference......
This series of layouts of Inspur Information may bring a new pattern to the large model industry.
EPAI will promote collaboration among the large model industry
Talking about the significance of the EPAI platform, Wu Shaohua introduced that the EPAI platform provides a toolbox and methodology, allowing users to maximize the value of their own enterprises.
Moreover, the EPAI platform will be able to achieve benefits for developers, and enterprises will no longer have to spend a lot of money to build a high-end team, and they can also build large models.
Through EPAI and the methodology it provides, enterprise users can start with their own set of methods and tools, and then go on to serve and support customers in their industry.
Only with the help of the most suitable and efficient tools, so that it has a high efficiency and success rate when working, can the industry really grow rapidly.
In this regard, Liu Jun, senior vice president of Inspur Information, said that EPAI "allows us to see the other side of the large model, the side of its industrialization".
In addition, EPAI is also beneficial for collaboration across the industry –
In the past, everyone wanted to be a big model company, and they all wanted to build a set of tools from scratch, but in fact, it was impossible for everyone to make their business bigger.
Only by relying on high-quality industrial collaboration and division of labor, everyone doing what they are best at, and forming a diversified and jointly promoted ecosystem, can industrial AI be truly implemented.
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