guide — AI 101

Everything That Actually Matters for Running Local AI

Artificial Intelligence is becoming more powerful every day, and many people are now interested in running AI models directly on their own computers instead of using cloud services. This is called "local AI". While it may seem complicated at first, understanding a few key concepts makes choosing the right model and hardware much easier.


What Is Local AI?

Local AI means running an AI model on your own computer instead of sending your data to an online service. This gives you more privacy, lets you work offline, and allows you to fully control your AI tools.


The biggest question asked is, "Can my computer run AI?" The answer depends on two things:

  • Can the model fit into your computer's memory?
  • Can it run fast enough to be useful?


Understanding these two questions helps you choose the right setup without wasting money.


Why VRAM Matters

Your graphics card (GPU) has its own memory called "VRAM". AI models are stored in this memory while they run. Larger models need more VRAM, but that doesn't always mean you need an expensive graphics card. Modern AI techniques allow models to use much less memory while keeping nearly the same quality. This means many people can run surprisingly capable AI models on hardware they already own.


Understanding Quantization

One of the most important concepts in local AI is quantization. Quantization reduces the amount of memory an AI model needs by storing its data more efficiently. The result is a much smaller model with very little loss in quality.


"Q4_K_M" is considered the best balance between quality and performance. It keeps the model small while producing results that are very close to the original. Going below this level saves even more memory, but the AI may become noticeably less accurate.


Context and Memory

When chatting with an AI, it remembers the conversation through something called the context window. The longer your conversation becomes, the more memory the AI uses. This extra memory is stored in something known as the KV cache.


Some tools allow the KV cache to be compressed, reducing memory usage with almost no noticeable drop in performance. This is especially useful for long conversations.


AI Engines vs User-Friendly Apps

There are two main ways to run local AI. The first is an AI engine, which does the actual processing. Engines offer the best performance and the most control but can be difficult for beginners.


The second is a wrapper, which provides an easy-to-use interface around the engine. Applications like Ollama and LM Studiomake it simple to download and run models. Behind the scenes, many of these tools use llama.cpp, one of the most popular AI engines for local inference. For new developers, starting with a wrapper is a great idea before learning the more advanced engine settings.


Bigger Models Can Run on Smaller PCs

Many people believe they are limited to small AI models if they own a budget computer. That's not always true. Some modern AI models use a design called Mixture of Experts (MoE). Instead of activating the entire model for every response, only the parts needed for the current task are used.


This allows extremely large models to run on computers with relatively small amounts of VRAM by sharing the workload between the GPU and the computer's normal RAM. In many cases, these large models can outperform smaller traditional models while still running at usable speeds.


Choosing the Right Hardware

If you plan to run local AI, don't assume you need to buy a brand-new computer. Test your current hardware first because many existing systems are already powerful enough for everyday AI tasks.


If you do decide to upgrade:

  • More VRAM is important for running traditional AI models.
  • Fast system RAM becomes more important when using offloading with Mixture of Experts models.
  • Used GPUs with higher VRAM can often provide better value than newer cards with less memory.

For many users, a mid-range graphics card offers an excellent balance between price and performance.


Picking the Right AI Model

There isn't one perfect AI model for everything.


Instead, choose a model based on your task. For example:

  • General chat models are great for everyday conversations.
  • Coding models are designed for programming.
  • Vision models understand images and screenshots.
  • Writing models produce higher-quality text.
  • Embedding models are useful for search systems and Retrieval-Augmented Generation (RAG).

Another important tip is to download Instruct or Chat versions of models instead of Base models, since base models simply continue text rather than acting like an AI assistant.


The Biggest Lesson

The most important takeaway is that local AI is about more than saving money. Running AI on your own computer gives you greater privacy, full control over your data, and freedom from depending on cloud services. For many everyday tasks, your current computer may already be capable of running powerful AI models without paying monthly subscription fees.


Final Thoughts

Local AI may seem overwhelming at first because of all the different models, settings, and technical terms. However, once you understand concepts like VRAM, quantization, context memory, and choosing the right model for the job, everything becomes much easier. Rather than guessing which model or hardware to use, you can make informed decisions based on your own needs. As local AI continues to improve, more people will be able to run powerful AI assistants directly on their personal computers, keeping their data private while enjoying fast, capable AI without relying on the cloud.