Many of us are familiar with popular public Large Language Models (LLMs) like ChatGPT, Claude, or Gemini and may even use them daily for work tasks. At LefeWare Solutions, we find LLMs helpful for creating marketing content, drafting webpage copy, and occasionally tackling technical questions. However, we ensure that each response from a public LLM is verified for accuracy.
One crucial point to consider: anything entered into public LLMs is shared with the model provider, which can inadvertently expose confidential information.
Additionally, public LLMs are trained exclusively on publicly available data, which sometimes lacks the depth needed for specialized, sensitive tasks. As a result, they may fall short of delivering highly accurate answers for more complex or proprietary needs.
For organizations that prioritize secure, accurate insights, setting up a private LLM might be a valuable next step.
What are Private LLMs?
A private Large Language Model (LLM) is an AI model designed specifically for an organization and hosted in a secure environment, ensuring data privacy and control. Unlike public LLMs, which are trained on vast, publicly available data, private LLMs can be customized with proprietary or industry-specific information, allowing them to generate more accurate and contextually relevant responses for internal use while keeping sensitive data secure.
What Tools and Platform Can I Use to Deploy a Private LLM
There are 3 options for creating your own private LLM going from most customizable and most setup – to least customizable and easiest setup
Option 1: Open-source Models on Private Infrastructure
If you’re going for maximum security and flexibility, hosting an open-source model like Meta’s Llama 2 on your private infrastructure is often the best choice. While setup may take longer and requires deep technical expertise, this approach grants you complete control over data handling and model customization.
Use this option if:
- Your team has deep technical and infrastructure knowledge
- your businesses prioritize data privacy and complete ownership of the model
- You require an extremely high level of accuracy in your responses
Option 2: Cloud Platforms
Using cloud-based platforms like Microsoft Azure OpenAI, Google Vertex AI, or AWS Bedrock offers a faster setup with enterprise security measures and a GUI sandbox type studio in place to test out operations. These platforms abstract away most of the underlying infrastructure providing a quicker and easier setup
Use this option if:
- You have some technical expertise
- You want a strong balance between ease of setup, security, and flexibility.
- You trust this cloud provider to safeguard your data privacy.
Option 3: Out of the box
Lastly, there are out-of-the-box, SaaS-based Generative AI Solution builders that offer the quickest and simplest setup, with model selection largely handled for you. These platforms are designed with intuitive user interfaces and advanced support to get your organization up and running with an LLM as seamlessly as possible.
One platform I frequently recommend to clients is GENii by Neural Hiive. It’s a reliable choice for teams who have minimal technical expertise but still need data security and guaranteed compliance within industry
Use this option if:
- You want to get setup as quickly as possible
- You have minimal technical knowledge on your team
Training and Feeding Your Own Data
So, you’ve deployed your own private large language model, and now you can prompt it securely. But as soon as you start asking highly specific questions—especially about niche details not available on the public internet—the answers still fall short of your expectations.
With a private LLMs you can train it using your own data by securely feeding in your company’s documents, emails, knowledge bases, or any other valuable internal data. The goal is to ensure the model understands your specific context, industry jargon, and unique business processes.
Let’s look at two common strategies for customizing a private LLMs:
RAG Model
The Retrieval-Augmented Generation (RAG) model uses external data retrieval systems to pull relevant documents and contextually feed them into the model at query time. It’s ideal for companies needing real-time accuracy with a smaller computational footprint. RAG is great for cases like customer service where precise information retrieval is key.
Fine-Tuning
Fine tuning is the most powerful approach, where we actually retrain sections of the model to integrate the nuances of your specific dataset. Fine-tuning works best for businesses that require a high degree of specialization. While it requires more computational power and expertise, it pays off with highly accurate responses.
Conclusion
Companies looking to harness the power of AI while protecting their data have plenty of options. However, no matter which route you choose for your private LLM, there’s going to be setup involved, and it can feel like a lot to handle without the right expertise. That’s where we come in at LefeWare Solutions. We’re technical experts; our team is packed with skilled engineers who know how to make this process smooth, efficient, and secure.
We’re always looking for innovative ideas on how to integrate these into your workflows
So if you have an idea—or if you’re just curious about where AI could take your business—click the link and It will take you to a landing page where you can share a few quick details.
Private Large Language Model Offer
Let’s talk, let’s brainstorm, and let’s make it happen! Don’t wait—AI is here, it’s more affordable than ever, and there’s no better time to get started.
