Practical Use Cases for Local LLM and Vision Models

May 26, 2025

Most articles about artificial intelligence are written by people who don't actually understand how it works. As a result, they miss both the real opportunities and the real limitations. At the same time, those who do understand AI are usually too busy building with it to stop and explain it. That gap creates confusion about what AI can actually do today. This article aims to bridge that gap—offering a clear, grounded look at how local language and vision models are already being used by those who work with them directly.

Local AI models—large language models (LLMs) and vision models—are powerful tools that can handle a range of creative and technical tasks directly on your own machine. Once you’ve set up a system capable of running these models, you unlock capabilities that extend far beyond simple chatbots. Here’s a look at what they can actually do today, how you might use them, and what their current limitations are.

The most common and versatile use case is text generation. LLMs can write computer programs, suggest code snippets, debug errors, and generate Bash or Python scripts. This makes them incredibly useful for automating repetitive tasks or building new tools. Beyond code, these models can help generate structured outlines, rewrite or improve drafts, translate between languages, and even simulate brainstorming sessions. If you're a writer, developer, or researcher, LLMs are like having an always-on assistant ready to help you think, fix, and write.

Another major use case is image generation. Vision models can create detailed, stylized images from text prompts. You can use them to design book covers, illustrate scenes or characters for novels, produce product mockups, or even create fantasy maps and concept art. These visuals are often good enough to serve as drafts or even finished pieces, depending on your standards and how much manual editing you’re willing to do.

Music generation is also becoming more accessible. AI can now produce songs that include both instrumentals and lyrics, tailored to a style or mood you define. These are useful for prototyping soundtracks, creating background music for games or videos, or experimenting with musical ideas if you’re not a musician yourself.

And yes, video generation is already possible. Though still in its early stages compared to text and images, AI can generate short video clips, animations, or video summaries. It’s being used to storyboard scenes, visualize abstract ideas, or produce video content from scripts. As tools improve, video will become an even more powerful medium for AI creativity.

However, to make these systems truly useful, they must be tied together—and that’s where automation comes in. Using Bash and Python, you can create pipelines that string together different models and processes. For example, you might use an LLM to write a script, pass it to a text-to-video model, and then use a vision model to generate stills—all in one automated flow. But even with automation, human editing remains essential. Whether it’s stitching outputs together, fine-tuning wording, or tweaking visuals, AI is a powerful collaborator—not a replacement for creative control.

One current limitation of local AI models is their scope. Most models generate only a page of text, one image, or a short clip at a time. To produce longer or more cohesive work, you’ll need to break it into pieces and then manually assemble and refine the results. Despite this, the flexibility and creative power of running AI models locally—without API limits, subscriptions, or surveillance—makes them an indispensable tool for creators, developers, and tinkerers alike.

If you want to get into AI seriously and actually understand and build things, follow these steps in order. First, install a Linux operating system—Ubuntu or Debian are good starting points. Linux is essential because nearly all AI development happens in a Unix-based environment. Once installed, get comfortable using the Bash command line interface (CLI); this is how you’ll control your system, install tools, manage files, and run AI code efficiently.

Next, install Ollama, a tool that allows you to run large language models (LLMs) locally. After installation, download one or more open-source models like Mistral or LLaMA. Running these models locally requires serious hardware—budget at least $2,000, though $4,000 is better. You’ll need a high-end GPU (NVIDIA RTX 3090 or better, or multiple GPUs), 64+ GB of RAM, and fast SSD storage.

This setup gives you full control of models, privacy, and the ability to experiment deeply—prompt engineering, fine-tuning, or building autonomous AI agents—without relying on cloud APIs. Once your hardware and system are set up, begin learning Python, the language most AI tools are written in, and start exploring model usage, data manipulation, and automation.

From here, you’ll have the foundation to actually do AI: run models, write scripts, analyze outputs, and build tools.

Here are the most relevant URLs to help you get started with AI using Linux, Bash, Ollama, and local models:

https://ubuntu.com/download/desktop – Official Ubuntu Desktop download page (recommended Linux OS for beginners)

https://linuxjourney.com/

– Free interactive guide to learning Linux basics

https://tldp.org/LDP/Bash-Beginners-Guide/html/ – Bash beginner's guide from The Linux Documentation Project

https://ollama.com/

– Official Ollama site to download and install the tool for running local AI models

https://ollama.com/library – Ollama’s model library (download open-source models like Mistral, LLaMA, Code LLaMA, etc.)

https://pytorch.org/

– The most commonly used deep learning framework, necessary if you move beyond prebuilt models

https://www.python.org/doc/ – Official Python documentation to start learning the programming language

https://huggingface.co/models – Massive repository of AI models (text, vision, audio) that you can run or fine-tune locally

https://github.com

– Where most open-source AI projects live; you’ll find model repositories, inference code, and datasets here

https://rentry.org/ollama-guide – Community-made unofficial guide with extra tips for installing and using Ollama effectively

These sites cover OS setup, Bash usage, Python learning, model hosting, and the tools you’ll need for real AI development work.

Your Opinion Matters: Help Me Improve My Writing

As an author, I'm always looking for ways to improve my craft and create content that resonates with readers like you. This week, I'm giving away some of my ebooks that got a bad review. But I believe that every book has the potential to connect with someone, and that's where you come in.

I'd love for you to take a look at these ebooks and share your honest thoughts with me. Your feedback is important, and will help me to refine my writing and create better content for you and other readers.

Here are the ebooks I'm giving away this week!

The Tao of Christ

https://www.amazon.com/review/create-review/?ie=UTF8&channel=glance-detail&asin=B0D8L7KZXV Like I said mea culpa.

Encirclement Warfare and Airland Battle

this is a work of popular non-fiction, not academic scholarship. It’s factually true: I wrote it to give ideas to Ukraine about how to do to Putin what happened to Hitler. I would greatly appreciate your review!

Medical Interpreter’s Dictionary

https://www.amazon.com/review/create-review/?ie=UTF8&channel=glance-detail&asin=B0D7FNJMSV

This is a multilingual medical dictionary: somehow, a Russian speaker was unhappy with it… Knowing my position on the Ukraine war that shouldn’t be surprising. A few five star reviews would be wonderful since I truly want medicos to be able to talk to patients in those languages I do know without bias or confusion interfering.

MANDARIN CHINESE WORD GAMES HANZITUTION

https://www.amazon.com/review/create-review/?ie=UTF8&channel=glance-detail&asin=B0DHS2RHJV

A reader probably expected more crossword puzzles even though the cover shows the logic of the book is to find Chinese characters that look at least something like Western characters and then use those as the basis for substitutions (Kou mouth w口rd for example)

Bitcoin: Digital Finance Law

One of my competitors didn’t read the table of contents and imagined everything possible to sink my book and push his. At the time I didn’t care though I would like to correct the record…