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The Beauty of Local LLMs

January 22, 20263 min read

Let me paint a picture. It's 11 PM. You're deep in a project. You need to bounce an idea off something smarter than a rubber duck but you don't want to copy sensitive code into a chat window owned by a company whose privacy policy you haven't read since 2023.

So you open a terminal and talk to a model running on your own machine.

No API key. No rate limit. No internet required. Just you and a few billion parameters doing their thing on local silicon.

That's the beauty of local LLMs.

Why local matters

The cloud is great. I'm not going to pretend otherwise. Services like ChatGPT and Claude are incredibly capable and I use them regularly. But there are real reasons to run models locally:

  • Privacy — Your prompts never leave your machine. For personal projects, work stuff, or anything sensitive, this matters.
  • Speed — No network latency. For small models, responses can be near-instant.
  • Cost — After the hardware investment, it's free. No per-token pricing. Run it all day if you want.
  • Control — You pick the model. You pick the parameters. You can fine-tune it on your own data.
  • Availability — Works offline. Works on a plane. Works when the API is down (and it will be down).

Getting started is easier than you think

A year ago, running a local LLM required jumping through hoops. Now? It's almost trivial.

Here's the simplest path:

# Install ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model
ollama pull llama3.2

# Start chatting
ollama run llama3.2

That's it. Three commands and you're talking to a capable language model running entirely on your hardware.

For something with a UI, tools like Open WebUI give you a ChatGPT-like interface that talks to your local models. It takes maybe ten minutes to set up with Docker.

What you can actually do with them

Local models aren't just toys. Here's what I use mine for regularly:

  • Code review and explanation — Paste in a function, ask what it does, get a solid explanation
  • Writing assistance — Drafting emails, editing blog posts (not this one though, I promise)
  • Brainstorming — Sometimes you just need something to riff with
  • Data processing — Summarizing documents, extracting structured data from messy text
  • Learning — Asking questions about topics I'm studying without worrying about looking dumb

The honest limitations

I'm not going to pretend local models match the frontier cloud models. They don't — not yet. Here's where they fall short:

  • Reasoning on complex tasks — GPT-4 class reasoning still needs GPT-4 class compute
  • Context window — Local models often have shorter context limits
  • Multimodal — Vision and audio capabilities are still catching up locally
  • Raw knowledge — Smaller models have less world knowledge baked in

But for 80% of daily tasks? A good 7B or 13B parameter model running locally is more than enough.

The future is hybrid

I don't think it's local or cloud. It's both. Use local models for quick tasks, private work, and experimentation. Use cloud models when you need maximum capability.

The important thing is having the choice. And right now, with the tools available, running your own AI has never been more accessible.

If you haven't tried it, give it a shot. Install Ollama, pull a model, and start a conversation. You might be surprised how good it feels to own the whole stack.

— Chuck