Latu Solutions · practical local AI

Local AI Setup Guide

Run AI on your own computer

You do not need an expensive AI server to start using local AI.

With the right model, many modern laptops and desktop PCs can run surprisingly capable AI directly on your own hardware.

This guide walks you through the simplest path:

Check your hardware install Ollama choose a model run your first local AI.

Check your hardware

Start with the hardware you already have

Local AI models run directly on your computer.

Instead of sending every prompt to a cloud service, the model runs using your own system RAM, GPU and VRAM, CPU and storage.

You do not need perfect hardware.

A smaller model running smoothly is often more useful than a huge model that barely fits.

Your hardware determines the practical model range, but model size is not the only factor. Quantization, context length and CPU/GPU offloading can significantly change memory requirements.

Choose a sensible first model

Hardware	Good starting range
8 GB RAM, no dedicated GPU	1B–4B quantized models
16 GB RAM, 6–8 GB VRAM	4B–9B models
32 GB RAM, 8–12 GB VRAM	8B–14B models
32–64 GB RAM, 16 GB VRAM	14B–24B models
64 GB RAM, 24 GB+ VRAM	24B–32B+ models

These are practical starting points, not hard limits.

A good quantized model can dramatically reduce memory requirements while preserving most of the useful capability.

Install Ollama

Ollama is one of the easiest ways to run local AI models.

It handles model downloads, storage and inference without requiring you to configure an AI framework manually.

Windows

Download and install Ollama from the official Ollama website. After installation, open PowerShell or Windows Terminal and check that it works:

ollama --version

macOS

Install Ollama using the official macOS installer. Then open Terminal and run:

ollama --version

Linux

Open Terminal and run:

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

Then:

ollama --version

Run your first model

Once Ollama is installed, launching a model can be as simple as:

ollama run qwen3:8b

This is only an example model. Your Hardware Checker result may recommend a different model for your system.

On the first launch, Ollama downloads the model. Depending on the model, this can require several gigabytes of storage.

Once the download is complete, you can start chatting directly in your terminal.

Try a first prompt

Explain how solar panels work in simple terms.

Write a Python function that sorts a list of dictionaries by date.

Summarize the following text and give me the five most important points:
[Paste your text here]

You now have an AI model running locally on your own computer.

Check whether your GPU is being used

A dedicated GPU can make local AI dramatically faster.

If you have an NVIDIA GPU, open a second terminal while the model is running and use:

nvidia-smi

Look for:

GPU memory usage
GPU utilization
Ollama-related process

ollama ps

If a model is running mostly on the CPU when you expected GPU acceleration, check your GPU drivers and Ollama installation before assuming you need stronger hardware.

Want a graphical interface?

The terminal is useful for testing, but most people eventually want a ChatGPT-style interface.

One popular option is Open WebUI.

It provides a browser-based interface for local models and can connect directly to Ollama.

Then add the graphical interface.

Every extra component adds complexity, so building the stack one working piece at a time makes troubleshooting much easier.

Open WebUI guide — coming later

Understanding quantization

AI models can be stored at different levels of numerical precision.

Quantization reduces the amount of memory a model needs.

For many local AI users, a good Q4 quantization offers an excellent balance between quality, speed and memory usage.

Higher precision usually requires more RAM or VRAM.

The biggest model your computer can technically load is not always the best model to use.

A slightly smaller model running comfortably often produces a much better overall experience.

Common problems

The model is extremely slow

Possible reasons:

the model is too large for your hardware
most inference is running on the CPU
your context length is too high
the system is using RAM instead of faster VRAM

Try a smaller model first.

Out of memory

Your selected model may require more RAM or VRAM than your system can comfortably provide.

Try:

a smaller model
a smaller quantization
reducing context length

My GPU is not being used

Check:

GPU drivers
Ollama GPU support
whether Ollama needs to be restarted
nvidia-smi
ollama ps

Do not immediately assume your GPU is incompatible.

My computer becomes unresponsive

The model may be consuming almost all available system memory.

Stop the model and choose a smaller one.

Leave enough memory available for your operating system and other applications.

The model runs, but the answers are poor

A larger model is not automatically the solution.

Performance also depends on:

the model's strengths
the prompt
quantization
the task
context
model generation

Different models can be better at coding, reasoning, writing, tool use or general conversation.

Privacy: what does “local” actually mean?

When the model itself runs locally, inference can happen entirely on your own computer.

That can be useful for:

private documents
company information
personal notes
source code
offline use

If you connect your local model to cloud APIs, web services, analytics or external tools, some information may still leave your computer.

Always understand where your data flows.

What should you try next?

Try a stronger model

Use the Hardware Checker to see what else your computer can realistically handle.

Use the Hardware Checker

Install Open WebUI

Add a comfortable browser-based chat interface.

Chat with your own documents

Build a private document-search or RAG setup.

Give your AI web access

Connect a local search engine or controlled web-search workflow.

Build local AI agents

Let models interact with tools and automate tasks.

But there is no need to build all of this on day one.

Start with one model and one useful task.

The next step

The Complete Local AI Guide is coming

This quick-start guide gets your first local model running.

But that is only the beginning.

We are building a much more comprehensive Complete Local AI Guide covering the entire practical local AI stack.

choosing the right model for your hardware
Ollama configuration and optimization
quantization explained properly
NVIDIA GPU setup and troubleshooting
Open WebUI
private document chat and RAG
local web search
local AI agents
model performance testing
privacy and security
upgrading RAM, VRAM and hardware intelligently
running larger models through CPU/GPU offloading
building a practical private AI stack from scratch

The goal is simple:

Take you from “Can my computer run AI?” to a complete local AI environment you actually understand and control.

Complete Local AI Guide — coming soon

No giant server required. No unnecessary complexity. Start with what you already have.

Check what your computer can run