Using an Open-source LLM for Enterprise Solution

Introduction

With the AI landscape constantly changing and the Large Language Models (LLMs) dominating the news cycles, most businesses are looking into utilizing AI to improve their business functionality and profit margins. With the latest batch of LLMs, we can see that they are exceptionally good at handling many tasks, such as analyzing data sets to garner insights, capable of text summarization and generations, which can streamline the business's customer interaction capabilities and reduce the burden on the employees, freeing up their time to focus on more creative aspects of the work that can future improve business growth.

But before using LLMs for enterprise-level solutions, it is essential to understand how to set up your Generative AI capabilities in the organization's current environment. In this article, we will explore using an open-source LLM for an enterprise solution, select the correct model that suits the business's use case, compare it with proprietary models, and examine deployment considerations and common pitfalls to be aware of.

Proprietary vs. Open-source LLM

Two significant differences exist between an open-source large language model and a proprietary large language model. The first one is the accessibility that open source LLMs are freely accessible to the general public. However, private corporations own proprietary models that control how an end user can access the LLM. To access such a model, the user often has to enter through paid APIs provided by the company or use their Web UI (For example, Gemini, ChatGPT).

Another significant difference is the customizability of the model. While proprietary models allow users to fine-tune specific models with their data, there is a severe limitation on modifying or inspecting the underlying model logic compared to open-source LLMs, which would enable the developer to check the underlying code and tailor it according to the business needs and, depending on the licensing of the model, even distribute it. Examples of popular open-source LLMs include LLaMA Variants, BERT, and Phi-3 Mini. 

One drawback with Open-source LLMs is that they require significant technological knowledge to implement as a solution. Still, the added advantage of tailoring the model to exactly meet an organization's business needs and having complete control of the data that interact with the model, along with much better transparency and greater control on infrastructure options that can be selected when integrating the LLM with the exciting solution, can offset this drawback.

Hosting an Open Source LLM

Before hosting an open-source model, several key points a business should take into consideration to avoid common pitfalls most organizations experience:

1. Infrastructure flexibility: It is essential that developers design an infrastructure capable of handling frequent changes and updates. This includes smoothly integrating new features into existing infrastructure and scaling the resources according to demand.
2. Selecting the Right Model: To get as much value as possible from integrating the model into your enterprise solution, it is important to select an LLM that best fits your business's use case. Before selecting one, evaluate models based on their capabilities, performance, and how well they align with your specific needs.
3. Licensing and Community Support: Check the licensing terms of the open-source model to ensure compliance and avoid any restrictions you might face when using it for your solution. Additionally, consider the level of community support available. An active community provides valuable resources, updates, and troubleshooting assistance, which is especially important when using open-source LLMs.
4. Plan for Maintenance: When designing the infrastructure, develop a proper maintenance plan for the model. Consider that the model will require regular maintenance that is up to industry standards.

Current Available options to host an Open LLM

When looking into options to host Open-source LLMs, there are multiple options the organization can look into for hosting their model. Here, we can look into some examples of available solutions,

1. Vertex AI Model Garden: Google's latest solution allows the user to select over 100 open-source models and serve them. Google handles the deployment of the LLM to an accelerator-optimized machine—this solution best suites users already with their infrastructure set up in the Google cloud platform.
2. Amazon SageMaker: Like the Model Garden, AWS provides SageMaker, which users can use to deploy their open-source LLM.
3. On-premise hosting: Another cool thing about using an Open Source LLM is that you have the option to set up the model in a server that is on-premise; while this solution requires significantly more technical knowledge to set up compared to the other two solutions, it gives the complete control over the model to the business.
4. Serving on a VM: Similar to hosting the solution on an on-premise server, you can host the solution on a VM server in the cloud. Depending on the existing solutions or your preference, you can select a service provider to procure a VM to host the Model. If you go with this solution, resource optimization is essential to avoid a large hosting bill.