How do I run a large language model on my own computer without expensive hardware?

You can run smaller, optimized versions of large language models (LLMs) on a regular laptop. You don't need a data center. The trick is using what are called 'quantized' models — think of them as compressed versions of the original AI. They trade a tiny bit of accuracy for a massive drop in hardware requirements. A tool called Ollama makes this surprisingly simple. You download it, open your terminal, and type something like `ollama run llama3.2`. It handles the rest. I've run a 7-billion-parameter model on a MacBook Air without it breaking a sweat. The key spec to watch is your RAM. 16GB is a comfortable starting point. 8GB works, but you'll be limited to very small models. The GPU matters less than you'd think for these compressed versions. Here's what actually happens: the model loads into your system memory, and your CPU does most of the work. It won't be as fast as ChatGPT. You might wait 10-20 seconds for a long response. But it's private, works offline, and costs nothing beyond the electricity. A specific example: the model 'Llama 3.2 3B' runs well on 8GB of RAM. 'Mistral 7B' needs closer to 16GB. Start with the smallest model that fits your task. Most people overestimate how much model they need. For summarizing documents or drafting emails, a 3-billion-parameter model is often plenty. One thing nobody mentions: your first download will be 4-8 gigabytes. Do it on Wi-Fi, not a mobile hotspot.