---
type: email
from: Tech With Tim <tim@techwithtim.net>
subject: how to run local LLMs in 2025
received: 2026-03-06 07:31:02
date_header: Sun, 14 Sep 2025 12:02:24 +0000 (UTC)
priority: normal
status: awaiting_approval
uid: 30376
---

## Email Content

**From:** Tech With Tim <tim@techwithtim.net>
**Subject:** how to run local LLMs in 2025
**Date:** Sun, 14 Sep 2025 12:02:24 +0000 (UTC)

### Body (preview)

Hey there,



Today’s email shows you a simpler way to use AI models without

relying on GPT for everything.



Because let's be honest...



You probably use GPT all the time, whether it’s debugging a

tricky script, explaining a concept you forgot, cleaning up messy

code, or even writing parts of your project.



But here's the catch...



All of that power comes from an API that you do not control...



That means you're stuck with their servers, their rules, and

their pricing. And once your workflow relies on it, it's hard to

walk away.



That's exactly where Ollama helps...



It is a free tool that lets you run powerful language models like

LLaMA 2 or Mistral directly on your local machine.



You install it once and then you can start a model with a single

command.



From there, you can send prompts to it from Python, from your

terminal, or even through a simple HTTP API.



You don't need to mess with Docker, fine-tune any models, or even

have a high-end GPU to get started, but keep in mind that models

like Mistral still work best on machines with enough RAM and a

decent CPU.



And once it's running, everything stays on your machine.



No data gets sent out, responses are fast, and you’re not charged

for every token.



And that makes a real difference when:



* You're working with sensitive data and want it to stay private.

​

* You're tired of waiting for something that should respond

instantly.

​

* You're working on something serious, and you cannot risk it

breaking just because someone updated their service.

​

* Or maybe you're just done relying on systems you cannot fully

control.



Here's a practical example.



Let's say you have a simple text file with common customer

questions like return policy, delivery times, or how to cancel an

order.



Instead of replying to each message manually, you want a small

script that can answer those questions instantly.



You don't want to send any data to GPT or pay for API calls.



You

---

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