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Large language models (LLMs) like ChatGPT and Copilot have become game-changers for many organizations. They offer opportunities to enhance and automate customer interactions, handle documents automatically, and make internal data more accessible for analysis and forecasting. The possibilities are impressive and almost limitless. And it's not just large enterprises that can benefit—small businesses with more modest budgets can leverage these tools too.
However, as the famous Dutch footballer Johan Cruijff once said, "Every advantage has its disadvantage." In the case of AI, training these LLMs is enormously taxing on the environment. Additionally, you’re sharing your data with major cloud providers, which is far from ideal and can even be a deal-breaker for companies with stringent data security requirements.
We offer solutions to both of these challenges: a private LLM hosted on Leafcloud. Keep reading to discover how you can develop a sustainable private LLM while retaining full control over your data.
Want to dive right in? Take a look at our step by step guide here.
What Is a Private LLM?
First, let's clarify what a private LLM is.
A private LLM allows companies to deploy an internally configured version of an AI chatbot (like ChatGPT) without sharing data externally. This means you have full control over your information, ensuring compliance with all privacy and data security regulations. You minimize the risk of data leaks, maintain control over your intellectual property, and prevent your data from reappearing elsewhere in the future.
This control benefits both your sensitive business data and your customer data. Suppose you want to automate or improve customer interactions. To do so, you'll need to input information about your current customer contacts. However, your customers trust that their data and interactions with you are secure. With a private LLM, you train an AI model internally using your data, eliminating the need to share any information externally.
Benefits of Private LLMs
Data control and privacy are crucial advantages of a private LLM, but there are many more.
Data Control and Privacy
To summarize: with a private LLM, you retain control over your data. Given that data security is a top priority for almost every organization today, keeping sensitive data in-house is a necessity. This ensures regulatory compliance and reduces the risk of data breaches.
Cost Efficiency
Private LLMs can also be cost-effective. They offer predictable costs, potential scalability benefits, and long-term savings compared to ongoing payments for external services. Moreover, they help you avoid vendor lock-in and the associated costs over time.
Customization and Relevance
A private LLM allows you to fine-tune the AI model with your own data. This makes the results more accurate than when using generic AI applications trained on external data. We’re all familiar with the hallucinations of ChatGPT. The more specific your LLM’s input, the more relevant the output will be for your organization.
You can also seamlessly integrate the model with existing systems through Retrieval-Augmented Generation (RAG). This means you can develop AI solutions that perfectly align with your existing processes and requirements.
Updates on Your Terms
With a private LLM, you decide which updates to implement and when. With a generic LLM, you’re at the mercy of the model owner, and updates may come either too soon or too late. Often, an update requires the entire model to be updated. With a private LLM, you can choose to retain older versions of embeddings and delay updates. While this isn't always advisable, it gives you better control over the timing.
Sustainable Training
Training and running AI models consumes vast amounts of energy and water. By choosing a private LLM, you can select your provider, allowing for more sustainable development if you opt for Leafcloud.
We reuse 85% of the residual heat from our servers to heat tap water in buildings with a central water heating system (a LeafSite). This approach saves energy because we don’t need to cool the servers, we avoid CO2 emissions from building new data centers, and residents use less fossil fuel for hot water. This approach can save up to 1,691 tons of CO2 annually per LeafSite—the equivalent of the annual energy consumption of more than 200 households. This way, you can train AI models more sustainably without compromising on performance.
Is a Private LLM Right for Your Business?
Setting up your own LLM has become much easier thanks to the wide availability of open-source tools and pre-trained models. While it still requires some technical expertise, the time you invest will pay off significantly. Training LLMs is no longer exclusive to large organizations with massive budgets.
If you're curious about the possibilities of private LLMs and have a basic understanding of relevant technologies, our step-by-step guide will help you get started.
Take Action for Growth and Future-Proofing
Private LLMs offer opportunities to grow your business and make it future-proof. This is the future, and at some point, you'll need to get on board. When you do, it's better to have control and do it sustainably than to outsource to American providers who use your data to train their models in even more massive data centers that guzzle energy and water.
Use our step-by-step guide. Or, if you need some assistance or have questions, feel free to contact us. We’ll address all your queries directly.
