What Local LLMs Are

In the last few years, the world has been captivated by Large Language Models (LLMs) like ChatGPT, Claude, and Gemini. These models are typically hosted in the cloud, owned by giant tech corporations, and accessed via APIs or web interfaces.

But there is a parallel movement growing just as fast: Local LLMs.

A Local LLM is a large language model that runs entirely on your hardware—your laptop, your desktop workstation, or your private server. It doesn't require an internet connection to think, it doesn't send your data to a third-party server, and it doesn't cost you a subscription fee per month.

Why Local LLMs Matter

The shift toward local AI isn't just for hobbyists; it's a fundamental change in how we interact with intelligence.

1. Sovereignty and Privacy

When you use a cloud-based LLM, your prompts—which might contain proprietary code, personal thoughts, or sensitive customer data—are sent to a remote server. Even with "privacy modes," you are ultimately trusting another company. Local LLMs ensure that data never leaves your machine.

2. Cost Efficiency

Cloud APIs (like OpenAI's GPT-4o) charge per token. For high-volume applications, automated agents, or RAG (Retrieval-Augmented Generation) pipelines that process thousands of documents, these costs can spiral into the thousands of dollars. Local LLMs cost only the electricity to run them and the initial hardware investment.

3. Customization and Control

Cloud models are "aligned" and "censored" according to the provider's policies. They can change (often called "model drift") overnight. With local models, you choose the version, you set the system prompt, and you decide exactly how the model behaves without surprise updates.

How Is This Possible?

Running a model with 7 billion or 70 billion parameters sounds like it requires a supercomputer. However, several breakthroughs have made this possible on consumer hardware:

Quantization: This is the process of reducing the precision of the model's weights (e.g., from 16-bit to 4-bit). This drastically reduces the RAM requirements with only a minor hit to intelligence.
Efficient Architectures: Models like LLaMA 3, Mistral, and Gemma are designed to be extremely "punchy" for their size.
Unified Memory: Modern chips, especially Apple's M-series (M1/M2/M3/M4), allow the GPU to share the same pool of high-speed RAM as the CPU, making them local AI powerhouses.

The Role of Ollama

Ollama is the tool that brought this power to the masses. Before Ollama, running a local model involved complex Python environments, CUDA configurations, and manual weight downloads.

Ollama turned this into a single command: ollama run llama3.

In this course, we will explore every corner of Ollama, from basic usage to building production-grade local AI platforms.

Hands-on Preview

Even if you haven't installed anything yet, think about your current hardware. Do you have a dedicated GPU (NVIDIA)? How much RAM does your computer have?

In the next lesson, we’ll compare the trade-offs between the local approach and the cloud approach you're likely used to.

Quick Knowledge Check

What is the primary difference between a Cloud LLM and a Local LLM?
What is "Quantization" in the context of local models?
Name two major benefits of running LLMs locally.

Key Takeaways

Local LLMs run on your own hardware, ensuring 100% data privacy.
Advancements in quantization allow powerful models to run on consumer-grade laptops.
Ollama simplifies the process of managing and running these models.

Module 1 Lesson 1: What Local LLMs Are