In the wave of digitalization, artificial intelligence (AI) has become a key technology for enterprise transformation and upgrading. However, the challenge faced by many enterprises is how to efficiently introduce AI into enterprises and integrate it with their existing technical architecture and business needs. As a leader in open source, Red Hat believes that open source is the best way to drive technology innovation and adoption. In the field of AI, Red Hat also adheres to this philosophy, and helps enterprises use AI technology more efficiently and maximize business value through open source.
A few days ago, at the 2024 Red Hat Media Open Talk, Cao Hengkang, global vice president and president of Red Hat Greater China, said that Red Hat is committed to building lightweight AI solutions by using smaller models and parameters to meet the actual needs of different scenarios and find the best balance between performance, cost and efficiency.
At present, the development of artificial intelligence is in the ascendant, and the debate about open source and closed source is becoming more and more intense.
Among them, under the closed system, the development of AI technology has made a series of breakthroughs. However, this system also brings with it the problems of ecological monopoly and security concerns. Because technology is in the hands of a few enterprises or individuals, it is easy to lead to technical barriers and monopolies, thus limiting the healthy development of the entire ecosystem. At the same time, AI technology in a closed system may have security risks because of the lack of open and transparent audit and supervision mechanisms.
In Cao's view, the advantages of open source lie in its transparency, flexibility, and customizability, which allows open source AI models to quickly adapt to various application scenarios and needs. In addition, open source fosters collaboration and innovation on a global scale, accelerating the advancement of AI technology.
In the author's opinion, open-source AI technology provides many advantages for enterprises. First, open-source technology lowers the barrier to AI adoption for enterprises. Instead of developing AI systems from scratch, enterprises can customize and optimize based on existing open-source frameworks and models to accelerate the development of AI applications.
At the same time, open-source AI technology has better transparency and customizability. Enterprises can gain insight into the inner workings of models, flexibly adapt and optimize to their business needs, and more importantly, open source AI technology fosters collaboration and innovation on a global scale. By participating in the open source community, enterprises can communicate and share experiences with developers around the world to promote the continuous progress and innovation of AI technology. This kind of cross-regional and cross-industry collaboration helps enterprises continuously expand the application boundaries of AI technology and achieve greater business value.
Taking large language model (LLM) as an example, as one of the important technologies in the field of AI, it has a wide range of applications in natural language processing, speech recognition and other fields. For example, the acquisition of data resources and the optimization of algorithms may be limited, which will affect the performance and application scope of LLMs.
In fact, the development and application of AI technology is not limited to the cloud, but gradually penetrates into various devices and scenarios, including edge computing and terminal devices.
That's why Red Hat recognizes the value of hybrid cloud in AI deployments. Hybrid cloud allows enterprises to flexibly migrate applications and data between public and private cloud environments, optimizing resource allocation, reducing costs, and increasing efficiency and flexibility based on business needs.
When it comes to developing and deploying AI models, a hybrid cloud strategy has brought significant benefits to Red Hat and its customers. First of all, it breaks the limitations of the traditional cloud computing model, so that AI models can not only be developed in the cloud, but also modeled in enterprise data centers, and at the same time, hybrid cloud also supports the deployment of built small language models to edge computing devices. This means that even in the absence of a stable network connection, these devices are able to run AI models independently to support real-time data processing and decision-making.
Cao believes that Red Hat's hybrid cloud strategy provides a new, more flexible and efficient solution for the deployment and application of AI, and by leveraging the advantages of public and private clouds and combining the capabilities of edge computing, Red Hat is helping enterprises build a more intelligent, efficient and sustainable AI ecosystem.
It can be seen that after Cao Hengkang elaborated on Red Hat's AI strategy, we can clearly extract two core keywords: open source and hybrid. These two concepts not only permeate Red Hat's AI solutions, but also reflect Red Hat's unique understanding and strategic positioning for the development of AI technology.
First, open source is an important cornerstone of Red Hat's AI strategy. At Red Hat, we believe that open source is the best way to drive technology innovation and adoption. In the field of AI, the strength of open source lies in its openness, transparency, and collaboration. Through open source, Red Hat brings together the wisdom and power of developers around the world to advance AI technology. Whether it's model development, model tuning, application development, or deployment, Red Hat uses open source technologies and tools to ensure that solutions are flexible, scalable, and sustainable.
Second, hybrid is another important pillar of Red Hat's AI strategy. The concept of hybrid has multiple meanings in the field of AI. On the one hand, it refers to the mixing of technologies, that is, the combination of different AI technologies and tools to solve complex problems. On the other hand, it also refers to a mix of environments, such as the flexible deployment and application of AI models in multiple environments such as public cloud, private cloud, and edge computing. This hybridity not only improves the adaptability and flexibility of AI solutions, but also provides enterprises with more options and possibilities.
Cao Hengkang emphasized that AI is not just about models, but a comprehensive system involving infrastructure, application layers, and models. That's why Red Hat's AI solutions are designed to provide a comprehensive, open-source, hybrid environment to support the AI needs of the enterprise at all levels.
It's worth mentioning that Red Hat has always focused on providing infrastructure, platforms, and tools, rather than hardware or application development. In terms of hardware support, whether it is internationally renowned chip manufacturers such as NVIDIA, AMD, Intel, or emerging chip companies in China, Red Hat is committed to adapting and cooperating with them.
In addition to hardware support, Red Hat provides comprehensive solutions at the infrastructure, platform, model, and application layers, where Red Hat Linux provides users with strong performance, stability, and security, while Red Hat's OpenShift and Ansible products also play an important role at this level, providing users with powerful capabilities such as containerized application management and automated operations.
Red Hat's goal is not only to provide a simple development environment, but to build a comprehensive, one-stop AI service platform. Through this one-stop service, Red Hat aims to lower the barrier to entry for AI technology and make it easy for more businesses and individuals to enjoy the convenience and value brought by AI. At the same time, Red Hat will work with many partners to promote the popularization and development of AI technology.