Introduction
Finding time to attend a one hour long webinar isn’t always easy, no matter how insightful the topic may be. That’s why we’ve distilled the key insights from our recent session on Local Large Language Models (LLMs) here. While you can still catch the full webinar on our YouTube channel, if you prefer a quick read of the key takeaways, we’ve got you covered.
What Are Local LLMs and Why Should You Care?
The impact of generative AI is undeniable. According to McKinsey, it has the potential to add between $2.6 trillion and $4.4 trillion in value to the global economy annually.
Local Large Language Models (LLMs) are becoming essential tools for businesses looking to leverage the power of AI while maintaining control over their data. These models operate within your own infrastructure, giving you full control over data security and customization. Unlike public models, Local LLMs can be fine-tuned specifically to your business needs, offering a unique advantage for companies handling sensitive or proprietary information.
To give you a clearer understanding, here’s a quick breakdown of key concepts you’ll need to know:
Generative AI
Refers to the type of AI that creates new content based on patterns it learns from large datasets.
Foundation Models (FMs)
are the underlying deep learning models that can be used right out of the box or fine-tuned for more specific tasks.
Large Language Models (LLMs)
focus on processing text and understanding the relationships between words, allowing them to summarize, extract, and generate relevant content.
While generative AI and foundation models offer broad capabilities, fine-tuning is where the real value lies for businesses. Fine-tuning allows you to take a pre-trained foundation model and further train it on your specific data, enabling the model to deliver highly relevant and accurate results tailored to your organization’s unique needs.
By fine-tuning LLMs, you can:
- Increase Precision: The model adapts to your data, providing results that align more closely with your specific tasks.
- Improve Efficiency: Automate processes that would otherwise require manual input, saving valuable time and reducing operational costs.
- Ensure Data Security: With Local LLMs, sensitive data remains within your control, ensuring privacy and regulatory compliance.
The Taker-Shaper-Maker Model: Choosing the Right AI Approach
When it comes to integrating AI into your business, not all approaches are the same. McKinsey’s Taker-Shaper-Maker model offers a useful framework for understanding the different levels of AI implementation, depending on your organization’s technical maturity and business goals.
Taker: This approach uses off-the-shelf models that require little to no customization. These pre-trained models, like GitHub Copilot or Adobe Firefly, are ideal for quick and simple tasks. While these models are easy to implement, they may lack the precision and flexibility required for more complex or sensitive tasks.
Shaper: Organizations that require more tailored solutions often adopt the Shaper approach. This involves taking existing AI models and fine-tuning them to align with your internal data and systems. It’s a middle-ground solution—cost-effective yet customizable enough to meet specific needs.
Maker: For organizations with the resources and expertise to fully invest in AI, the Maker approach involves building custom models from scratch. This requires a significant commitment in terms of both data and computing power, but the payoff is a highly specialized AI model designed to meet very specific business needs.
Which Approach Is Right for Your Business?
Choosing the right approach for implementing LLMs depends not only on your technical needs but also on your budget.
- If your goal is to implement a quick, cost-effective AI solution with minimal customization, the Taker approach might be the best fit. It allows for a faster rollout with lower upfront costs, making it ideal for businesses that need a simple, ready-to-use solution.
- For businesses with more specific needs that require fine-tuned models, the Shaper approach offers greater flexibility. This option balances performance and customization with a moderate budget, enabling you to tailor the model to your internal data without the higher costs of building a model from scratch.
- If your organization has advanced technical capabilities and a higher budget, the Maker model provides full control over AI development. This approach demands a larger investment but delivers a highly specialized solution that can be perfectly aligned with your business goals and unique requirements.
Understanding Costs: What to Expect When Implementing LLMs
Before implementing Large Language Models (LLMs), it’s essential to understand the costs involved. The right approach depends on your business needs, the level of customization required, and your budget. While McKinsey’s Taker-Shaper-Maker model provides a helpful framework for estimating these costs, the figures we’ve included below reflect our experience working with clients who leverage outsourcing partners like Ralabs. This approach can significantly reduce costs compared to a conservative estimate based on in-house development by large U.S.-based teams.
One-Time vs. Recurring Costs
When budgeting for LLMs, it’s important to differentiate between one-time setup costs and recurring expenses for model maintenance and scaling. For instance, in our fintech client’s case, we carefully evaluated both upfront costs and long-term operational expenses to ensure the solution remained cost-effective while delivering high performance.
During our implementation, we also tested several models, including GPT-4o and Llama 3. Ultimately, we chose GPT-4o mini for its excellent performance and affordability. At just $300 per month, it offered high-quality results without the heavy computational costs associated with larger models like Llama 3, which required $18 per hour of computing power.
By optimizing the system’s infrastructure and selecting the most cost-effective model, we helped the client balance affordability and performance while ensuring long-term scalability.
Ralabs solutions
Discover how we can implement a custom AI solution tailored to your specific business needs and help you save time and costs.
Best Practices for LLM Implementation
Define Clear Goals
Use Quality Data
Implement Retrieval-Augmented Generation (RAG)
Monitoring Performance
Challenges and How to Avoid Them
Model Degradation and Hallucinations
Over time, LLMs can experience model degradation, where the quality of responses diminishes. This can happen when the model starts producing irrelevant or less accurate outputs due to changes in your data or business context. Another common issue is hallucinations, where the model generates information that isn’t grounded in the data or reality. These hallucinations can be especially problematic in sectors like healthcare, legal, or finance, where accuracy is paramount.
To prevent these issues, it’s essential to regularly retrain and fine-tune the model, especially as new data becomes available. Monitoring performance metrics can help catch early signs of degradation, while clear prompt engineering and RAG (Retrieval-Augmented Generation) can reduce hallucinations by providing the model with specific, relevant data to draw from.
Security Risks and How Local LLMs Protect Sensitive Data
One of the biggest concerns when implementing LLMs is data security, especially when dealing with sensitive information. Public models can expose your data to third-party access or use your data to retrain their models, creating potential privacy breaches.
Local LLMs offer a strong solution to this issue. Since they run on your infrastructure, you maintain complete control over your data, ensuring that it’s never shared or exposed outside your organization. Sensitive data remains protected, and you can apply additional layers of encryption or security protocols to meet your specific regulatory requirements.
Solutions for Ongoing Model Optimization and Security Compliance
To keep your LLM operating at peak performance, ongoing optimization is essential. Regularly updating the model with new data, retraining it when necessary, and applying fine-tuning adjustments will help avoid model degradation. Additionally, integrating strong monitoring systems can flag performance issues before they escalate, allowing you to proactively address them.
On the security front, adopting a local deployment of LLMs ensures that all sensitive data stays within your organization’s control. Regular audits, encryption of stored data, and strong access controls should be part of your security strategy to meet both internal and external compliance standards.
Ralabs solutions
Concerned About LLM Risks?
Dive deeper into the primary risks associated with Large Language Models and learn how to mitigate them effectively.
Real-Life Example: Integrating LLMs for a Fintech Client
At Ralabs, we recently worked with a U.S.-based fintech client facing a significant challenge: managing the labor-intensive process of compliance audits. The client needed to complete 25 compliance surveys annually, each requiring an average of 16 hours of manual work. With subject matter experts charging $100 per hour, the process was both time-consuming and expensive, adding up to $40,000 annually in audit costs.
Our Solution: Leveraging LLMs to Automate Compliance
To address this challenge, we proposed an AI-driven system using Local LLMs to automate the compliance process while ensuring data security. We evaluated several platforms and ultimately chose Azure OpenAI and AWS Bedrock for this project, based on their robust capabilities and scalability.
Why Azure OpenAI and AWS Bedrock?
- Azure OpenAI was selected for its extensive support for GPT models, including GPT-4o and GPT-4o mini, which provided the high performance we needed for this specific application. Azure OpenAI offered a user-friendly interface for fine-tuning models, ensuring that the LLM could generate context-specific responses based on the client’s internal compliance documents. Additionally, Azure’s integrated security features were critical for handling the sensitive nature of the compliance data.
- AWS Bedrock was chosen as a secondary platform due to its reliability and stability. AWS Bedrock allowed us to integrate the compliance data securely, with a strong focus on scalability and resilience. Although AWS offers fewer models compared to Azure, its robust infrastructure made it an ideal backup option for ensuring continuous, error-free operations. AWS Bedrock’s serverless architecture also simplified deployment and maintenance, making it easier for the client to manage the system long-term
Results:
The results were transformative. The AI-driven system reduced the audit time from 16 hours to just 3 hours per survey. In total, the client saved 325 hours annually, equating to $32,500 in annual cost savings. Over a three-year period, these savings would amount to nearly $100,000. Furthermore, the AI/ML infrastructure we developed is reusable, allowing the client to scale the system to automate other tasks beyond compliance.
Use Our Expertise to Implement LLMs in Your Organization
At Ralabs, we’ve successfully implemented Local LLMs for clients across industries, tailoring AI-driven systems to their unique needs while ensuring privacy, security, and scalability. Our expertise in fine-tuning models and optimizing infrastructure allows us to deliver high-performing solutions that align with your business goals.
Check out our case studies for more details, or let’s talk about how LLMs can work for you—schedule a free consultation call.
