Running Large Language Models Locally
Artificial Intelligence (AI) is rapidly transforming the way organisations operate, and large language models (LLMs) are at the forefront of this evolution. In a recent webinar hosted by FlowMoCo, our Enterprise Architect, Ben Whorwood, in his role as Enterprise Architect in Residence at Canopy Community, shared invaluable insights into the practical applications of running LLMs locally. Whether you’re a tech enthusiast, a non-technical founder, or a community organisation like Canopy Community, understanding how to leverage local AI models could be a game-changer.
Running LLMs Locally: A Practical Revolution
For many, the idea of running AI models locally may sound complex, but Ben introduced a solution that simplifies the process—Ollama.
Ollama enables users to run LLMs directly on their local machines, whether they’re using Mac, Linux, or Windows. The best part? No deep technical expertise is required. The installation process is straightforward and doesn’t even need administrative permissions, making it accessible for founders, small teams, and individuals looking to integrate AI into their operations without external dependencies.
With Ollama, experimenting with AI is now within reach for everyone.
Why Should You Run LLMs Locally?
Opting to run LLMs locally offers several compelling advantages:
- Enhanced Data Privacy
Keeping your data in-house reduces the risk of leaks or breaches, ensuring sensitive information remains secure.
- Greater Customisation
Local deployment allows organisations to fine-tune AI models to meet specific operational needs without relying on generic cloud-based solutions.
- Cost Efficiency
Say goodbye to ongoing cloud subscription fees—once set up, local models offer long-term savings with minimal running costs.
Ben emphasised that while running AI locally may seem intimidating, with tools like Ollama, the process is more accessible than ever before.
Addressing Common AI Challenges
One of the most frequently discussed challenges with AI models is hallucination—where an LLM generates plausible-sounding but incorrect responses. This issue often worsens under heavy usage, leading to misleading outputs that could impact decision-making.
Ben highlighted a practical solution: adjusting model parameters such as temperature, which controls the randomness of responses. Setting the temperature to zero, for example, ensures that the model provides consistent answers for identical prompts—ideal for structured business processes.
Looking to the Future
As AI technology continues to evolve, the possibilities for leveraging local LLMs are expanding rapidly. Whether it’s automating repetitive tasks, enhancing customer interactions, or generating insightful content, the potential is vast.
At FlowMoCo, we’re committed to helping organisations navigate this evolving landscape with practical insights and accessible solutions. Stay tuned for future discussions where we’ll explore even more real-world applications of AI.
Interested in exploring how local LLMs can transform your operations?
Get in touch with Ben Whorwood today to learn more about integrating AI into your organisation and unlocking its full potential.
This article is part of our ongoing AI Insights series, dedicated to making AI technology practical and accessible for businesses of all sizes. Stay connected with us for more updates and expert advice.
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