The rise of large language models (LLMs) has made things a lot more convenient—writing, coding, summarizing, you name it. Most people have tried out the popular ones online, but now there's a growing trend of using local LLMs. That basically means running the model on your computer, not through a cloud service. While it sounds like a smart move for privacy or control, it's not always the smoothest ride. So, let's break down what it really means to use a local LLM and whether it makes sense for your setup.

Should You Use a Local LLM? 9 Pros and Cons

You’re in Control of Your Data

One of the first things people notice with local LLMs is the level of privacy. Whatever you’re feeding into the model stays on your machine. No need to worry about your prompts, documents, or chat history going into some company’s servers. If you’re working with sensitive materials—client notes, proprietary code, or anything confidential—this is a big plus.

But keep in mind, just because it’s local doesn’t mean it’s automatically safe. If your device isn’t secured properly, the data is still at risk. You just eliminate one layer of exposure.

No Internet? No Problem

When you're using a local model, you don't need to be connected to the internet for it to work. This can be a relief in areas with patchy connections or for people who travel and still want AI assistance on the go.

Still, some people expect the local model to have live info like the weather or the latest stock prices, and that's not going to happen. Local models don't browse or update in real time. They work with what's already in their training data.

Customization Comes Easier

Another solid reason to use a local model is that you can tweak it. Fine-tune it on your data, adjust the way it responds, or even trim it down to just what you need. It becomes a tool that actually fits how you work, not the other way around.

This works best if you know what you’re doing. The process isn’t impossible, but it does involve a little technical know-how. If you’re new to this, you might need to spend some time learning the ropes before you get the results you’re looking for.

There’s No Cost Per Prompt

Once you’ve got the model up and running, there’s no meter ticking every time you use it. That’s a big deal if you rely on LLMs for a lot of small tasks every day. While hosted services often offer free tiers, those usually come with limits, and premium access isn’t cheap.

Of course, the cost shows up elsewhere—mainly in the hardware. Bigger models need a decent GPU and lots of RAM. So even though you don't pay per prompt, setting things up might not be cheap upfront.

Set-Up Can Be Frustrating

Installing a local LLM isn't like downloading an app and clicking 'open.' You'll need to know how to install dependencies, handle model weights, and maybe even mess around with system settings to get it running right.

Some newer tools are trying to simplify this with pre-built launchers or easy installers, but for the average person, there’s still a learning curve. If you’re not used to working with code or terminals, this part might get a bit annoying.

Updates Don’t Happen Automatically

Hosted models are always being updated, sometimes even daily. With a local LLM, you get what you downloaded—unless you go back and manually pull in a new version. If you want your local model to stay current, you’ll need to keep track of updates yourself.

This isn’t always a big issue if your use case doesn’t rely on the latest facts. But if you’re expecting the model to know recent news or respond to newly popular questions, you’ll start to see the gaps pretty quickly.

Performance Varies a Lot

Here’s where things get real: a local LLM only performs as well as your hardware allows. If you’ve got a strong GPU and enough RAM, you’ll be fine. But if you’re trying to run a large model on a laptop from a few years ago, it’s going to lag—or not work at all.

Some lighter models are surprisingly fast and do a decent job with common tasks. But for in-depth reasoning or long conversations, you’ll want something more powerful. And more power means more memory, more space, and more heat.

You’re Not Sharing Resources

One benefit that doesn’t always get talked about is that you’re not in a queue. With online tools, especially free ones, your session might slow down if a lot of people are using the system at once. That’s not the case with local models. Everything is running just for you.

It makes the experience more consistent, especially when you're working on a deadline, or you just need quick answers without lag. But again, that consistency depends entirely on your machine.

It's a bit of a Tinkerer's Playground

Some people genuinely enjoy the process of running models locally. It becomes a hobby—testing different models, combining tools, and even modifying how the model talks or what it prioritizes. If that sounds like fun, local LLMs offer a lot of room to experiment.

But if you're just looking for a plug-and-play assistant and don't care about the inner workings, this probably isn't the path for you. Local models reward curiosity and patience more than they reward quick solutions.

So, Should You Use One?

If privacy, customization, and one-time cost matter more to you than convenience or up-to-date info, a local LLM could be a good fit. It's especially worth exploring if you’ve got the hardware and don’t mind a bit of setup time.

But if you want something that just works out of the box, updates itself, and has the latest information baked in, sticking with a hosted service might be the better option. There's no one-size-fits-all answer here—it comes down to what you're comfortable managing and what you actually need the model to do.