More than three decades ago, Red Hat saw the potential of open source development and licensing to create better software and foster IT innovation. Thirty million lines of code later, Linux has not only developed to become the most successful open source software, but it also maintains that position to this day. The commitment to open source principles continues, not only in the corporate business model, but also as part of the work culture. In the company's assessment, these concepts have the same impact on artificial intelligence (AI) if done correctly, but the technology world is divided on what the "right way" would be.
AI, especially the large language models (LLMs) behind generative AI (gen AI), cannot be viewed in the same way as an open-source program. Unlike software, AI models consist primarily of numerical parameter models that determine how a model processes inputs, as well as the connection it makes between various data points. Parameters of trained models are the result of a long process involving vast amounts of training data that are carefully prepared, mixed, and processed.
Although model parameters are not software, in some respects they have a function similar to code. It is easy to compare the data to the model's source code, or something very close to it. In open source, source code is commonly defined as the "preferred way" to make modifications to the software. Training data alone does not fit this function, given its varying size and the complicated pre-training process that results in a tenuous and indirect connection that any item of data used in training has with the trained parameters and the resulting behavior of the model.
Most of the improvements and enhancements to AI models currently occurring in the community do not involve accessing or manipulating the original training data. Instead, they result from modifications to model parameters or a process or adjustment that may also serve to fine-tune model performance. The freedom to make these model improvements requires that parameters be released with all the permissions that users receive under open source licenses.
Red Hat's vision for open source AI.
Red Hat believes that the foundation of open source AI lies in open source licensed model parameters combined with open source software components . This is a starting point for open source AI, but not the ultimate destination of the philosophy. Red Hat encourages the open source community, regulatory authorities, and industry to continue striving for greater transparency and alignment with open source development principles when training and tuning AI models.
This is Red Hat's vision as a company that encompasses an open source software ecosystem and can practically engage with open source AI. It's not an attempt at a formal definition, like the one the Open Source Initiative (OSI) is developing with its Open Source AI Definition (OSAID). This is the corporation's perspective on how to make open source AI feasible and accessible to the widest possible range of communities, organizations, and vendors.
This perspective is put into practice through work with open source communities, highlighted by the InstructLab , led by Red Hat, and the effort with IBM Research on the Granite family of licensed open source models . InstructLab significantly reduces the barriers for non-data scientists to contribute AI models. With InstructLab, domain experts from all sectors can add their skills and knowledge, both for internal use and to help create a shared and widely accessible open source AI model for upstream communities.
The Granite 3.0 family of models addresses a wide range of AI use cases, from code generation to natural language processing to extracting insights from large datasets, all under a permissive open source license. We helped IBM Research bring the Granite family of code models into the open source world and continue to support the family of models, both from an open source perspective and as part of our Red Hat AI offering.
The repercussions of DeepSeek's recent announcements show how open-source innovation can impact AI, both at the model level and beyond. Obviously, there are concerns about the Chinese platform's approach, particularly that the model's license doesn't explain how it was produced, reinforcing the need for transparency. That said, the aforementioned disruption reinforces Red Hat's vision for the future of AI: an open future focused on smaller, optimized, and open models that can be customized for specific enterprise data use cases in any location within the hybrid cloud.
Expanding AI models beyond open source.
Red Hat's work in the open source AI space goes far beyond InstructLab and the Granite family of models, extending to the tools and platforms needed to actually consume and productively use AI. The company has become very active in fostering technology projects and communities, such as (but not limited to):
● RamaLama , an open-source project that aims to facilitate the local management and deployment of AI models;
● TrustyAI , an open-source toolkit for building more responsible AI workflows;
● Climatik , a project focused on helping to make AI more sustainable when it comes to energy consumption;
● Podman AI Lab , a developer toolkit focused on facilitating experimentation with open source LLMs;
The recent announcement about Neural Magic broadens the corporate vision for AI, making it possible for organizations to align smaller, optimized AI models, including licensed open-source systems, with their data, wherever they reside in the hybrid cloud. IT organizations can then use the vLLM to drive decisions and production from these models, helping to build an AI stack based on transparent and supported technologies.
For the corporation, open source AI lives and breathes in the hybrid cloud. The hybrid cloud provides the flexibility needed to choose the best environment for each AI workload, optimizing performance, cost, scale, and security requirements. Red Hat's platforms, goals, and organization support these efforts, along with industry partners, customers, and the open source community, as open source in artificial intelligence is propelled forward.
There is immense potential to expand this open collaboration in the AI space. Red Hat envisions a future that encompasses transparent work on models, as well as their training. Whether next week or next month (or even sooner, given the rapid evolution of AI), the company and the open community as a whole will continue to support and embrace efforts to democratize and open up the world of AI.
