Local LLM Execution with Ollama & Model Runtime Internals

Introduction

Running Large Language Models (LLMs) locally is the first step to understanding their behavior and resource requirements. This lesson introduces Ollama as a tool for local LLM execution.

What is Ollama?

Ollama is a lightweight, extensible framework for building and running language models locally. It abstracts the complexities of model deployment and provides a simple CLI and API.

Model Runtime Internals

When an LLM runs, it loads model weights into memory (RAM or VRAM). The runtime manages token generation, context windows, and memory allocation. Understanding quantization (e.g., 4-bit or 8-bit) is crucial as it significantly reduces memory requirements while maintaining reasonable accuracy.

Running Your First Model

You can pull and run models with simple commands like ollama run llama3. This automatically handles downloading the model weights, configuring the runtime, and exposing a conversational interface.

System Resource Implications

Local execution requires careful monitoring of system resources. We will explore how memory utilization spikes during inference and how to adjust context sizes to fit within available hardware limits.