Opinion: Practical Local LLM Usage in 2026

In 2026, we've been exposed to large language models such as ChatGPT, Claude, Perplexity, Copilot, Gemini, and Siri on a daily basis. The latest LLMs are actually really effective on most topics. It's truly the dawn of the LLM and ML (machine learning). However, not all of the uses are positive.

There's a common piece of knowledge that "if the product is free, you are the product". This advice rings truest with LLMs, where you trade the ability to converse with an all-knowing internet connected advisor in exchange for your data privacy. While some people seem to not care about their data potentially being read or used to train LLMs, companies have a very bad record of not being hacked when they have valuable data... just check haveibeenpwned to see if your email credentials have been compromised by hackers.

With this in mind, it is my opinion that businesses and individuals who would like to protect their information should have a local LLM. This recommendation comes not only with the motivation of protecting privacy, but also an increase in the customization of your LLM to suit your own needs.

Before I continue, you should know my credentials that I stand on to make this recommendation. I was a developer of one of the first modern LLMs for business. I touched every aspect of it from model creation and weighting to heading up the team that combed through the datasets to be fed to the model. This "AI" made it into production and revolutionized my area of the tech sphere. Additionally, I run my own local LLM now and have been developing it for a while.

The title of this article promised practical LLM usage, which is something I find lacking in many of the current writing and YouTube videos. I think there may be one somewhat relevant result when searching for "practical LLM". I plan on giving you these practical approaches to making your life easier with local LLMs, but first we need to understand which applications make sense.

LLMs are large language models that incorporate datasets of sentences and words. The model uses this dataset to train itself on "what word comes next". Based on frequency of certain words following other words in the dataset, the LLM "learns to speak". It effectively has no idea what it's saying, but it has weights and parameters that it uses to determine the "intent" of what you're saying and respond with that same intent. These models lately have been really good at cohesive sentences and responding with authority on many topics. These responses are only as good as the dataset the model was trained upon. 

With that in mind, here's some strong use cases of a local LLM:

1. Coding
   I tried using an LLM to code and then compared the code to ChatGPT. The project was very complex and required a lot of specific knowledge. The results were shockingly similar. The code was almost identical, which is interesting because I'm sure no one else has a similar project that it could have stolen code from online, which it commonly does. This took the limits of coding large programs that require a lot of context and allowed me to use my own resources. This also keeps my coding projects private, so if I have a program containing industry secrets, there's no way that the data will be leaked through my chats.
   
   
   
2. Car Repair
   I work on cars, as will be made obvious in later articles. I wanted a way to easily ask questions to a chatbot that could answer without me having to pull up the service manual and slowly look through it for exactly what I need. Instead, I fed an LLM model a database of forum posts and service manuals on my vehicle. Before giving it this data, it effectively just made-up responses and hallucinated vehicle facts. After this data augmentation, it gives me responses directly from trustworthy sources and includes links to the sources so I can read further if I have additional questions.
   
   
   
3. LLM "Vision"
   There are certain models that you can use to describe images from your camera. With less powerful GPU devices, the models you'll be able to run will not be extremely smart. However, it can be useful for writing image captions or explaining what's probably going on in a scene. I wouldn't use this info without double-checking over it, but it is pretty cool for basic images.
   
   
   
4. Business Usage
   If you have a business or a client business curious about LLMs, this is an excellent usage of LLMs. It's a hot market right now for building LLM servers for support chat bots, client acquisition, employee training, a "second opinion" for leadership as far as trickle down effects of their management decisions. When it's as easy as uploading data from a company's long history to analyze decisions and act as an expert without having to release trade secrets or look silly asking certain questions, it's such an easy decision to use an LLM to increase business efficiency.
   
   
   
5. Specific Education
   This is what I've been testing recently. You can have expert LLM profiles for certain topics. For example, I added a manual for a complicated project I was working on to the dataset and was immediately able to ask questions about the manual and leverage internet connectivity of the LLM to gain further context for part names I wasn't sure about or describing the functions of certain parts. This is an excellent usage of LLM's and actually one I was a part in developing, which I'll talk about in another post. 

While I'm only including these 5 examples, they are great inspiration of what you can use an LLM to do. They cover creation, specialization with specific datasets, journeying into real-life subject interpretation, and making money with LLMs. With the creation of quantized models, it's possible to run at least some kind of LLM on almost any hardware at the expense of some "intelligence". It's even possible to run an LLM without a GPU by leveraging CPU and RAM to process the models.

I hope you enjoyed reading and look forward to new articles on executing some practical LLM projects.

- Nurem