Running Your Own Large Language Models Locally: Privacy, Cost Savings & Customisation

A few months ago, I was talking with a client who faced a common dilemma. They needed to process sensitive medical records using AI, but trusted cloud services were simply not an option. Privacy concerns and soaring API costs made a compelling case for exploring alternatives. That’s when I discovered the beauty of running open‑source models like Llama locally.

Why Bother Running Your Own LLM?

Relying on third‑party AI services often means sacrificing control over your data. When you run your own large language model (LLM) locally, you decide exactly how your information is managed. No more worrying about sensitive records being sent off to unknown servers—what happens on your machine or cloud stays on your machine or cloud. This approach not only enhances security but also brings significant cost savings. Instead of paying recurring fees, you invest in hardware or a dedicated cloud instance.

There’s also a creative advantage. Running your own model means you can fine‑tune it to speak your industry’s language—be it medical terminology, legal jargon, or technical lingo. Customisation like this can set your business apart by ensuring the AI understands your specific needs. For further insights into choosing the right LLM, check out SSW’s Do You Pick The Best Large Language Model For Your Project?

Getting Started with Llama

Llama, now at version 3.3, is a standout model. It’s available in various sizes (from 7B to 70B parameters), supports up to 128,000 tokens of context, and even includes built‑in JSON output capabilities—all under a commercial‑friendly license. If you’re interested in exploring different models and making an informed choice, the SSW article on choosing the best LLM is a great resource.

Before diving in, consider the hardware requirements:

• 7B Model: At least 16GB of RAM
• Larger Models (13B+): 32GB or more
• GPU: A solid NVIDIA GPU is typically recommended (Running a powerful MacBook Pro also worked acceptably)
• SSD Storage: Around 20GB or more depending on the model

You can set up Llama locally by cloning its GitHub repository and installing the dependencies. If local hardware isn’t ideal, cloud options like Azure AI Foundry, Replicate.com, Hugging Face, Vast.ai, RunPod, or Hyperstack offer competitive pricing for high‑performance GPU access.

Fine‑Tuning: Making It Your Own

Fine‑tuning your LLM is like teaching a skilled employee new tricks. It involves preparing training data and choosing a method that suits your resources—whether that’s full‑parameter fine‑tuning, LoRA, or even the lighter QLoRA option. This customisation ensures your AI is perfectly aligned with your business needs. For more on best practices in AI customisation, see SSW’s Rules to Better AI Development.

Personalising Your AI: RAG vs Post‑Training

When it comes to incorporating personalised data into your AI workflow, there are two primary strategies: Retrieval‑Augmented Generation (RAG) and post‑training.

RAG (Retrieval‑Augmented Generation) is ideal for data that changes frequently. Think of seasonal updates, real‑time company news, or any information that needs to be refreshed regularly. With RAG, the model can dynamically pull in current data during each query, ensuring that the responses are always up‑to‑date.

Post‑Training, on the other hand, involves fine‑tuning the model on a specific dataset so it “learns” your particular context. This approach is perfect for static data or use cases where you want the AI to become highly specialised in your domain. Once the model is trained with your unique data, it can offer responses that are finely tuned to your business requirements.

Choosing between RAG and post‑training depends on your use case. If your data is dynamic and frequently updated, RAG is the way to go. For more stable, context‑rich data that benefits from deep customisation, post‑training offers a smart, tailored solution.

Real-World Success Stories

There are plenty of examples where local LLMs have made a real difference:

• A law firm using a fine‑tuned Llama 7B for contract analysis.
• A medical practice processing patient records securely with a customised AI.
• A manufacturing company predicting maintenance needs with precision.

Common Gotchas and How to Dodge Them

We have banged heads against the wall so you don’t have to. Here are some pitfalls to watch out for:

• Memory Management: Keep an eye on VRAM usage—these models can be as thirsty as a camel in the outback.
• Temperature Settings: Start with a lower value (around 0.7) to ensure focused outputs.
• Prompt Engineering: Good outputs require good prompts; invest time in crafting them.

The Bottom Line

Running your own LLM is more than a tech trend—it’s a strategic investment in your business. The benefits in terms of privacy, cost savings, and customisation are clear. If you’re ready to take control of your AI, start with a smaller model like Llama 7B. Test it on a limited dataset, learn the process, and scale up gradually.

And if you need a little guidance along the way, SSW offers a free one‑hour consultation to help you map out the best strategy for your business. Book your free consultation and let our experts help you design the perfect upgrade plan.

By taking these steps, you’re not just keeping up with the times—you’re shaping your future, securing your data, and making a smart investment in your business’s growth.