Llama

AI Glossary

Llama is Meta’s family of open-weights large language models — the most popular choice for businesses that want to run AI on their own servers instead of relying on a cloud service.

What it really means

Llama (Large Language Model Meta AI) is a series of AI models that Meta released for free. Unlike ChatGPT or Google’s Gemini, which are locked inside their respective companies’ servers, Llama’s “weights” (the trained numbers that make the model work) are available for anyone to download and run. I help businesses think of Llama like a raw engine — you get the blueprints and the parts, but you still need to bolt it into your own car.

The key word here is “open-weights.” It doesn’t mean the training data is open or that the model is truly open-source in the strictest sense. But it does mean you can take Llama, put it on a computer in your office or a rented server, and use it without sending your data to anyone else. That’s a big deal for businesses that handle sensitive information — medical records, legal documents, customer lists.

Meta releases Llama in different sizes, from small models that can run on a laptop (3B or 8B parameters) up to massive ones that need serious hardware (70B or 405B parameters). The smaller ones are fast and cheap to run; the larger ones are smarter but cost more in compute power.

Where it shows up

You probably haven’t used Llama directly — most people interact with it through other tools. It’s the engine inside many self-hosted AI assistants, custom chatbots, and internal knowledge base tools. When a company says they’re “running their own AI,” there’s a good chance it’s a version of Llama under the hood.

I’ve seen it used in a few common setups around Central Florida:

• A Winter Park dental practice runs a small Llama model on a local computer to draft patient follow-up emails without sending patient names to the cloud.
• A Maitland HVAC company uses a medium-sized Llama model to answer technician questions about repair manuals — all running on a server in their back office.
• Several local developers I know use Llama through services like Ollama or LM Studio to test ideas before building custom tools for clients.

Llama also shows up in academic research, open-source projects, and as the base for many fine-tuned specialty models. If you hear about “Code Llama” or “Llama Guard,” those are variations built on top of the original Llama.

Common SMB use cases

For small and medium businesses in Orlando, Llama is most useful when you need privacy or control. Here’s where I’ve seen it make sense:

• Internal document search — A law firm in downtown Orlando uses a Llama-based tool to let paralegals ask questions about past case files stored on their own network. No data ever leaves their office.
• Customer support triage — A Lake Nona restaurant runs a small Llama model to draft replies to common online reviews, keeping the tone consistent without handing review data to a third party.
• Data extraction from PDFs — A Sanford auto shop uses Llama to pull part numbers and labor estimates from scanned invoices, running everything on a $2,000 desktop computer.
• Custom training on your data — Because Llama is open-weights, you can fine-tune it on your own documents. A Clermont pool service company trained a small Llama model on their service manuals and pricing sheets — now their office staff can ask “How much for a pump replacement in Windermere?” and get an instant answer.

The big advantage for SMBs is cost. Running a small Llama model costs only the electricity and hardware — no monthly API fees, no per-token charges. For a business processing a few hundred queries a day, that can save hundreds of dollars a month compared to cloud AI services.

Pitfalls (what gets oversold)

Llama is powerful, but I’ve seen businesses trip over a few common misunderstandings:

• “It’s free, so it costs nothing.” The model is free to download, but you still need hardware to run it. A decent server for a medium-sized Llama model can run $3,000-$8,000. And you’ll need someone who knows how to set it up — that’s not free either.
• “I can run the biggest model on my laptop.” The 405B parameter model needs about 800GB of GPU memory. That’s not a laptop — that’s a small server room. Most SMBs should stick with the 8B or 13B models unless they have serious hardware and a real need.
• “It’s as smart as ChatGPT.” The largest Llama models are competitive with GPT-4, but the smaller ones (which most businesses can actually run) are noticeably dumber. They hallucinate more, follow instructions less reliably, and struggle with complex reasoning. You get what you pay for in compute.
• “I can just download it and it works.” Raw Llama doesn’t have a nice chat interface. You need to build or buy a front-end, set up the inference server, and handle things like prompt formatting and context windows. It’s not plug-and-play for most non-technical teams.

The biggest mistake I see is a business buying a server and downloading Llama thinking they’ve “solved AI” — only to realize they still need weeks of setup and tuning to get useful results.

Related terms

• Open-weights model — Any model where the trained parameters are publicly available, not just accessible through an API. Llama is the most famous example.
• Fine-tuning — Taking a pre-trained model like Llama and training it a bit more on your own data to make it better at your specific tasks.
• Self-hosting — Running AI software on your own hardware instead of using a cloud service. Llama is the go-to choice for self-hosting.
• Inference — The act of running a model to get a prediction (like generating text). Running Llama on your server is doing inference locally.
• Ollama — A popular tool that makes it easy to download and run Llama models on a Mac or PC without needing to be a machine learning engineer.