Self-hosted large language models for enterprise use
Large language models (LLMs) such as ChatGPT continue to gain ground, but the use of such models within companies often falls into a gray area. Companies face the difficult task of choosing the right infrastructure and ensuring that it meets the high demands of LLMs without compromising the security and confidentiality of their data. Self-hosted LLMs running on your own cloud or hardware offer an optimal solution for these needs.
Challenge
Large language models (LLMs) such as ChatGPT continue to gain ground, but the use of such models within companies often falls into a gray area. Self-hosted LLMs that run on your own cloud or hardware offer an optimal solution for this need. The biggest challenge is providing suitable and operational hardware / cloud infrastructure. This must not only be available, but also ensure the necessary drivers and access to multiple graphics cards in order to run the models effectively. Companies face the difficult task of choosing the right infrastructure and ensuring that it meets the high requirements of LLMs without compromising the security and confidentiality of their data.
Methods
To overcome the challenges of implementing self-hosted LLMs, we tested various models based on three specific use cases: IT support, tender analysis, and content creation. These tests were conducted on different platforms, including Python, olama, Azure, our own hardware, and AWS. First, we set up the necessary hardware or cloud infrastructure to ensure that it met the required technical specifications.
Once the infrastructure was in place, the models were hosted quickly and efficiently. We used self-generated data generated by GPT-4 to perform automated tests and evaluate the performance of the models in the various use cases. These automated tests allowed us to test the models under real-world conditions and measure their effectiveness. We then conducted a manual evaluation phase in which the models were compared in detail. The test data was used to obtain a comprehensive overview of the performance of the various models and to determine which models were best suited for the respective use cases.
Solution
Our solution offers your company numerous advantages. By using self-hosted LLMs on company-owned hardware or cloud infrastructure, rapid implementation and stable operation of the models could be ensured. This enables companies to retain control over their data while increasing efficiency and productivity. Self-hosted LLMs offer the advantage that sensitive company data does not have to be transferred to external service providers, which increases data security and meets compliance requirements. Companies have full control over the models and can customize them to their specific requirements and use cases. In the long term, self-hosted LLMs can be more cost-effective, as there are no ongoing fees for using external services. The models can be scaled as needed and used flexibly for various applications such as IT support, tender analysis, and content creation. The implementation of self-hosted LLMs in a corporate context has led to improved efficiency and productivity by automating routine tasks and delivering high-quality results.
- Rapid implementation and operation: Using self-hosted LLMs on company-owned hardware or cloud infrastructure ensured rapid implementation and stable operation of the models.
- Data protection and security: Self-hosted LLMs offer the advantage that sensitive company data does not have to be transferred to external service providers, which increases data security and meets compliance requirements.
- Adaptability and control: Companies have full control over the models and can customize them to their specific requirements and use cases.
- Cost savings: In the long term, self-hosted LLMs can be more cost-effective as there are no ongoing fees for using external services.
- Scalability and flexibility: The models can be scaled as needed and used flexibly for various applications such as IT support, tender analysis, and content creation.
- Increased efficiency and productivity: The implementation of self-hosted LLMs in a corporate context has led to improved efficiency and productivity by automating routine tasks and delivering high-quality results.
At BREDEX, we use state-of-the-art AI technologies to develop customized solutions for our customers. Our expertise in implementing and evaluating large language models enables us to offer innovative and effective solutions that create real added value. Contact us to learn more about our projects and our approach.
This project was conducted as part of the i3systems AI Experts.
These projects might also interest you:
Hyperskill
Optimize data management
