On-Prem Large Language Models

Discover a hybrid approach to combining pre-trained models with custom data on-prem. To ensure data privacy and intellectual property protection, deploy on-premises solutions like VectorDBs, embeddings, and Large Language Models (LLMs).

tecpeopleAI

7/6/20241 min read

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Balancing Flexibility, Efficiency and Privacy

To balance the trade-offs between Custom LLMs and pre-trained models, we propose a hybrid approach that leverages the strengths of both. For real-time applications like financial forecasting, we can use pre-trained models as a foundation and fine-tune them using custom data to adapt to specific business needs. This approach allows for efficient processing and analysis while still providing flexibility to adjust the model's performance.

Optimising Performance with On-Prem Solutions

To optimize the performance of AI-driven financial forecasting tools, we recommend utilising on-premises solutions that ensure data privacy and intellectual property protection within organisational boundaries. Here's how:

On-Premise VectorDBs: Deploy a VectorDB like Elasticsearch or OpenSearch on-premises to store and retrieve historical market data efficiently. This allows for fast querying and analysis of large datasets while keeping sensitive information within the organisation.
Embeddings: Incorporate embeddings, such as word2vec or BERT, to represent complex financial concepts and relationships in a nuanced manner. This enables the model to capture subtle patterns and trends in the data.
On-Premise LLMs: Utilize on-premises Large Language Models (LLMs) that are trained on proprietary data and can be fine-tuned for specific business needs. This ensures that sensitive information remains within the organization's control.

By integrating these on-premises solutions, businesses can:

Maintain Data Privacy: Keep sensitive financial data and intellectual property within organisational boundaries, ensuring that no data is exposed outside of its bounds.
Control Model Development: Fine-tune LLMs using proprietary data to develop models tailored to specific business needs, without relying on cloud-based services or exposing sensitive information.
Optimize Performance: Leverage the power of VectorDBs and embeddings to process vast amounts of historical market data efficiently and accurately.

In today's digital landscape, businesses need solutions that prioritise privacy, security, and intellectual property protection while still enabling the development of sophisticated AI-powered applications. On-premises solutions offer a reliable and secure way to achieve these goals, ensuring that sensitive information remains within organizational boundaries.