Has anyone explored private instance of LLMs for their own organisation and what are the pros and cons for it?

We have one, however we still do not allow company proprietary information to be used for queries

We have one. Works well but costly.

Yes, private instances of LLMs are being explored by many organizations, especially those prioritizing data privacy, customization, and control. The pros? You can fine-tune the model for your specific needs, ensure sensitive data stays internal, and avoid dependency on external providers' updates or outages. The cons? It's expensive, requiring significant investment in infrastructure, ongoing maintenance, and expertise. Also, keeping the model updated with new advancements can be resource-intensive.

For a more scalable and cost-effective option, consider Retrieval-Augmented Generation (RAG) approaches. With RAG, you combine smaller models with your own data in real-time, so you're not reliant on maintaining a massive standalone LLM. It can give you some of the benefits of customization without the operational headaches.

In both cases, make sure that whatever data you are feeding the LLMs is cleared to be used that way. 

Ultimately, try piloting to see what aligns with your goals and resources...

Exploring private instances of LLMs can offer significant benefits, but it also comes with its challenges. 

Here’s a quick breakdown:
Pros:
1️⃣ Data Security: Hosting LLMs privately ensures sensitive organizational data remains within controlled environments, enhancing compliance and confidentiality.
2️⃣ Customization: Tailoring the model to your specific industry or organizational needs increases relevance and efficiency.
3️⃣ Performance Control: Private instances allow fine-tuning and optimization to align with specific workflows and demands.
Cons:
1️⃣ Infrastructure Costs: Running LLMs privately requires significant investment in hardware, cloud resources, and maintenance.
2️⃣ Expertise: Managing and maintaining these models demands skilled teams for fine-tuning, updates, and troubleshooting.
3️⃣ Scalability: Scaling private instances to handle larger workloads can be complex and resource-intensive.

Ultimately, the decision depends on balancing these factors against the strategic value it provides to the organization. Starting with a pilot program can help assess feasibility and ROI.

We've explored private LLMs for building ASL interpretive services. Standing up the LLMs themselves is not the issue. Chris Lucian pointed out many of the pros/cons, so I'll refrain from repeating them. That said, the additional significant pros to me boil down to IP ownership with self-grown LLMs and the ability to tune them for accuracy on your specific use case. The cost savings could outweigh the high upfront cost if the scale gets large enough. Cons include the aforementioned high start-up cost and lack of specific use case data available at the volume needed to train your LLM accurately.