In today's fast-paced and competitive business environment, surviving is complicated. A new disruption that shakes industries occurs every year. Among these, the latest are open-source large language models (LLMs) that power generative AI. These machine learning models enable organizations to transform operations, enhance customer experiences, and boost innovations.

What is special about open-source LLMs is that their weights and training data are open to inspect, build upon, or redistribute. This provides you with full control over the technology you want to develop.

However, understanding the differences between “open” and open-source LLMs, how they differentiate from private LLMs, and deciding what is best for you can be challenging. 

Therefore, in this article, we will explore why open-source LLMs play a critical role in the era of generative AI, comparing different available choices, and how it enables organizations to innovate without the constraints of proprietary LLMs.  

Open-source vs open LLMs

The reality is that it is hard to determine what defines an open-source large language model. Generally, open-source technology is completely open for everyone to use. This is more complex with generative AI because LLMs are trained using a large amount of data. The simplified process goes like this:

• Data collection: An ever-increasing amount of data is collected from various sources such as the Internet, emails, customer purchases, design secrets, etc.
• Training data: This means taking the data and combining it to build new models or combining it with existing models, also known as fine-tuning. 

A fully open-source LLM is one where the training data is freely available to everyone. Easily inspect and change each component of a model to meet your needs, build upon it, and distribute it to others.  

This is sometimes not possible with certain open-source LLMs.  For example, for Llama 3.1 the dataset is not available, but is called an open-source model. There is not an accepted definition in the market.

Open-source vs private LLMs

With private models, you can take advantage of generative AI capabilities through using their API-based services. But you don’t have any control. You won’t know what data was used to develop their models or what happens in the backend. For example, there is no way to make sure that sensitive, or copyrighted data is not getting into the training data of these models. If you cannot control the model, you should avoid them unless you want to accept legal, compliance, or privacy risks.

This is not the case with open-source models. You have a lot more flexibility here. For example, you can run them in your own local data centers or private cloud environments. That means the data never leaves your premises. This makes it completely safe to use along with sensitive data. It is the only way to control each part of generative AI application development and ensure the highest security standards.

Types of LLMs and how open they really are

There are many providers on the market, each marketing their solution as the best one. It all comes down to this: are they secure and cost-efficient or not? To answer that we must analyze whether they are open source or closed. Because the former is more secure and cost-efficient, including a couple of other benefits, which we will cover later. 

Here are the most popular LLM models: 

Closed models

These are paid API services. They are restricted and controlled by the organization that has developed them. The biggest downside is that you can’t inspect the data or code. API-based LLM models from OpenAI's GPT and Anthropic’s Claude fall into this category.

*OpenAI started as an open-source project, with the main objective of making AI available to everyone at the time when Elon Musk was part of the team. However, it has since transitioned into a closed, paid model.

Open, restricted models

Then we have open-source models with certain restrictions. They may have limitations for specific use cases such as military, large user bases, or only allow non-commercial use. One example is Meta’s Lama 3.1 model: "You must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights".

Open-source – licensed models

These are also free for commercial use without restrictions, but they do not make all their training data available for inspection, due to certain licensing restrictions. The Granite family of models provided by IBM research and the Mistral AI fall under this category. You can use them, but they utilize the Apache 2.0 license. Some of these are more flexible than others.  

Open-source models

These are models that are truly “Open source”. You can inspect the model's data and the code. For example, OLMo by The Allen Institute for AI (AI2) that you can download on Hugging Face and in GitHub falls into this category. However, often these models don't have enough data access, which means they are not good enough. The more quality data the model has, the better its performance. But there isn't just enough free data to make these that good, yet. As a result, the licensed models are mostly used, even by us.

Importance of Open Source LLMs

When organizations want to build AI applications on their own terms, in their local data centers without relying on cloud-based solutions that are overly priced, open-source models become essential. These models are often smaller, providing cost efficiency and enhanced security as you can run them in your secure location. 

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Let’s explore each of the three benefits:

Control: When you use private LLM, you cannot track with what data the model was trained, nor know how your data will be processed when you use it. How can you trust these systems? You simply can't! Open source eliminates these concerns as it provides full access to everything. Hence giving control back to the organizations. And it's about time!

Cost-efficiency: Open-source LLMs are a lot smaller, and therefore provide a more efficient way to train, run, or fine-tune them. This creates a more accessible environment for experimenting with PoCs and scaling applications.

Security: You can run open-source models in your local data centers or private cloud environments. This benefit is one of the reasons why organizations choose open-source LLMs and why we exist - enabling the use of open-source to make the world safer.

How ConfidentialMind can help

It is only a matter of time when you find your competitor using new AI technology to outpace you. Cost savings and additional revenue gains are not hard to miss. That means they will have even more resources to increase their competitive advantage. To avoid staying behind, you need to adopt similar capabilities.

However, many will realize that internal confidential data permits using API-based LLMs for security, legal, or privacy concerns.

The second option is to use open-source LLMs. But this means developing Enterprise AI software infrastructure from the ground up - it is an overly complex and time-consuming task.

The third option is to use ConfidentialMind. Our Generative AI infrastructure and copilots for R&D are designed for on-premises data centers or private cloud environments. It is the most secure, fastest, and cost-efficient way to start your generative AI journey.

Regardless of your option, it’s time to get started!

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