How to Run AI Models Locally on a PC: Beginner’s Guide

How to Run AI Models Locally on a PC: A Complete Beginner’s Guide

Can you run AI models locally on your PC without sending your prompts to a cloud AI service? Yes. With tools such as Ollama and LM Studio, you can download compatible AI models and run them directly on a Windows, Linux, or macOS computer.

Running AI locally can be useful when you want more control over your data, offline access, lower recurring costs, or a private environment for experimenting with large language models (LLMs).

In this guide, you’ll learn how to run AI models locally on a PC, what hardware you need, which software to use, how to install a model, and how to choose a model that your computer can actually handle.

Quick Answer: How Do You Run AI Models Locally?

To run an AI model locally:

1. Check your PC’s RAM, GPU/VRAM, CPU and storage.
2. Install a local AI application such as Ollama or LM Studio.
3. Download a compatible AI model.
4. Load the model into memory.
5. Start chatting with the model locally.
6. Adjust the model size and settings if responses are slow or your PC runs out of memory.

Once the model files are downloaded, some local AI applications can operate without an internet connection. For example, LM Studio states that chatting with downloaded models, local document processing and its local server can work offline.

What Does “Running an AI Model Locally” Mean?

Running an AI model locally means that the model’s inference happens on your own computer instead of sending every prompt to a remote AI server.

For example, when you use a cloud-based chatbot, your request normally travels to the provider’s infrastructure, where the model processes it.

With a local LLM:

Your PC → Local AI software → AI model → Response

The model files are stored on your computer and your CPU and/or GPU perform the computation.

Popular local AI models include families such as Llama, Qwen, Mistral, Gemma and DeepSeek, although the exact models available and their hardware requirements change over time.

LM Studio currently supports running several local LLM families and provides Windows, macOS and Linux versions.

Why Run AI Models Locally?

Local AI isn’t automatically better than cloud AI. It is simply a different way of using AI, with different trade-offs.

1. Better control over your data

When a model is running locally, your prompts can be processed on your own machine.

For example, you could use a local model to summarize a document without uploading that document to a cloud AI service.

LM Studio specifically states that, when using downloaded models, chat data stays on the user’s device.

However, always check the privacy behavior and settings of the particular application you use.

2. Offline AI

After downloading the application, model and required components, you can use compatible local AI software without an active internet connection.

This can be useful while traveling, working in locations with poor connectivity, or processing sensitive documents locally.

3. No per-request API bill

Local models don’t require you to pay a cloud provider for every API request.

You still have hardware, electricity, storage and potentially GPU-upgrade costs, but there isn’t necessarily a usage-based API charge.

4. Experimentation

Local AI is particularly useful for developers and enthusiasts who want to experiment with:

• LLMs
• AI coding assistants
• RAG
• embeddings
• local APIs
• AI agents
• automation
• document analysis

Ollama, for example, provides a local API that applications can use to interact with models running on your computer.

What Hardware Do You Need to Run AI Locally?

The biggest mistake beginners make is assuming that every AI model can run on every PC.

The actual requirements depend heavily on the model size, quantization, context length and hardware acceleration being used.

As a practical starting point:

These are general guidelines rather than universal requirements.

For example, LM Studio currently recommends at least 16GB of RAM and 4GB of dedicated VRAM for Windows PCs.

A model that technically loads may still be frustratingly slow, so “can run” and “runs comfortably” are two different things.

RAM vs VRAM: What’s the Difference?

Understanding RAM and VRAM is important before downloading a large model.

RAM

System RAM is your computer’s main working memory.

If your model doesn’t fit efficiently into GPU memory, some or all of the workload may use system RAM.

VRAM

VRAM is memory attached to your graphics processor.

A GPU with more VRAM can generally accommodate larger models or larger portions of a model on the GPU.

Simple example

Imagine you have:

16GB system RAM + 8GB GPU VRAM

You have more flexibility than someone with:

8GB system RAM + integrated graphics

But this doesn’t mean you can automatically run every model advertised as “8GB.”

Other factors—including model format, context size and application overhead—also matter.

What Is a Model’s Parameter Size?

You will often see local AI models described as:

• 1B
• 3B
• 7B
• 8B
• 14B
• 32B
• 70B

The B means billion parameters.

For example:

8B = approximately 8 billion parameters

Generally, larger models can require substantially more memory and computing resources.

But parameter count alone doesn’t tell you exactly how much memory a model will need.

That’s where quantization becomes important.

What Is Quantization?

Quantization reduces the numerical precision used to represent a model’s weights.

You may encounter formats such as:

• Q4
• Q5
• Q6
• Q8

A lower-bit quantized model can require considerably less memory than a higher-precision version.

The trade-off is that lower quantization can affect quality depending on the model and workload.

For beginners, a well-regarded quantized model that comfortably fits their hardware is often more practical than trying to run the largest possible model.

Read Here: Ollama vs LM Studio: Which is Better for Running Llama 3 on a Mac?

The Easiest Ways to Run AI Models Locally

Two popular approaches are:

Option 1: Ollama

Ollama is particularly attractive if you prefer a command-line interface and want to integrate local models with development tools.

It is available for Windows, macOS and Linux.

Option 2: LM Studio

LM Studio is useful if you prefer a graphical interface.

It lets you discover, download and load compatible models through its desktop application. It supports Windows, macOS and Linux.

Method 1: How to Run AI Models Locally With Ollama

Step 1: Download Ollama

Download and install Ollama for your operating system.

On Windows, Ollama runs as a native application and supports NVIDIA and AMD Radeon GPUs. The current Windows documentation lists Windows 10 version 22H2 or newer as a requirement.

Step 2: Open the Terminal

On Windows, open:

PowerShell or Command Prompt

Then verify that Ollama is available.

ollama

Ollama’s current quickstart provides an interactive menu from the ollama command.

Step 3: Run a Model

Ollama allows you to run models from the command line.

For example:

ollama run gemma3

The exact model name you use should be checked against the models currently available through Ollama.

When the model is downloaded and loaded, you can start chatting with it directly from your terminal.

Step 4: Start Asking Questions

You can now type something like:

Explain quantum computing in simple language.

The model generates the response locally.

Depending on your hardware, the first response may take longer while the model loads into memory.

Method 2: How to Run AI Models Locally With LM Studio

If you don’t want to work primarily from a terminal, LM Studio is an easier starting point for many beginners.

Step 1: Install LM Studio

Download LM Studio for your operating system.

The official documentation currently supports:

• Windows
• macOS
• Linux

Step 2: Open the Discover Section

After installation, open LM Studio and use its model discovery interface.

You can search for models such as:

• Qwen
• Llama
• Mistral
• Gemma
• DeepSeek

The exact model selection should depend on your hardware and intended use. LM Studio’s documentation explains that downloaded model files are loaded into your computer’s memory when you run them.

Step 3: Download a Model

Choose a model that fits your available RAM/VRAM.

Don’t automatically download the largest model.

If your PC has limited memory, start with a smaller model.

A smaller model that responds quickly is often more useful than a huge model that constantly swaps data between memory and storage.

Step 4: Load the Model

Go to the chat interface and select the model you downloaded.

LM Studio describes loading a model as allocating memory to accommodate the model’s weights and other parameters.

Once the model loads, you can start chatting.

Read Here: How to Set Up a Completely Offline AI Assistant with LM Studio

Can You Run AI Models Without a GPU?

Yes.

You don’t necessarily need a dedicated GPU to run a local AI model.

A sufficiently capable CPU and enough system RAM can run smaller models.

However, CPU-only inference can be considerably slower than GPU-accelerated inference for many workloads.

If you’re experimenting with local AI for the first time, a smaller model is usually the sensible starting point.

Can You Run AI Models on 8GB RAM?

Yes, but your choices will be limited.

An 8GB computer can potentially run smaller, highly compressed models, but you should not expect the same performance or model selection available on a 32GB or 64GB machine.

You also need memory for:

• Windows/Linux/macOS
• the AI application
• the model
• context
• other running applications

So having an “8GB model” does not mean an 8GB PC has enough memory to run it comfortably.

Can You Run AI Models on 16GB RAM?

Yes. 16GB is a much more practical starting point for local AI.

LM Studio currently recommends at least 16GB RAM for Windows PCs.

With 16GB RAM, you can experiment with smaller and some medium-sized quantized models, depending on the model architecture and settings.

If you’re buying a new PC specifically for local AI, however, more RAM provides greater flexibility.

How Much Storage Do Local AI Models Need?

Storage requirements vary considerably.

The application itself may be relatively small, but AI models can consume much more disk space.

Ollama’s Windows documentation notes that model storage can reach tens to hundreds of gigabytes, depending on how many models you download.

Therefore, consider an SSD with enough free space before building a large local model collection.

A practical setup might include:

• 512GB SSD — entry-level
• 1TB SSD — comfortable starting point
• 2TB+ SSD — useful for multiple models and datasets

What Are the Best AI Models for a Local PC?

There is no single “best” local AI model for everyone.

Your choice should depend on:

Hardware + task + model quality + speed + license + context requirements

For example:

Model families and releases change frequently, so check the current model documentation before choosing a specific version.

Local AI vs Cloud AI

Neither approach is universally better.

Cloud AI is convenient and can provide access to very large models without requiring expensive local hardware.

Local AI gives you greater control over the computing environment and can be useful for offline or privacy-sensitive workflows.

Can Local AI Work Completely Offline?

Yes, with some important qualifications.

You generally need internet access initially to:

1. Download the AI application.
2. Download the model.
3. Download any required runtime or updates.

After the necessary files are already on your computer, compatible local AI applications can operate offline.

LM Studio explicitly documents offline operation for downloaded models, local document processing and its local server.

Ollama also runs models locally and exposes a local API on localhost for applications that integrate with it.

Can You Chat With Your Own Documents Using Local AI?

Yes.

This is one of the most useful applications of local AI.

For example, you can use a local AI system to work with:

• PDF files
• notes
• research papers
• documentation
• text files
• private knowledge bases

This commonly involves RAG (Retrieval-Augmented Generation).

Instead of asking the model to memorize your entire document, the system retrieves relevant sections and provides them to the model as context.

LM Studio currently supports chatting with documents locally using RAG.

How to Make Local AI Run Faster

If your model is running slowly, try these steps.

1. Use a smaller model

A smaller model generally requires fewer resources.

2. Use an appropriate quantization

A quantized model can significantly reduce memory requirements.

3. Reduce context size

Very large context windows can increase memory usage.

4. Use GPU acceleration

If your software and hardware support GPU acceleration, moving more computation to the GPU can improve performance.

5. Close unnecessary applications

Browser tabs, video editors and other memory-intensive applications can compete with the model for RAM.

6. Use an SSD

Keep your operating system and model files on an SSD rather than a slow mechanical hard drive.

Common Problems When Running Local AI

“Out of memory”

Your model or context is demanding more memory than your system can comfortably provide.

Solution: Try a smaller or more aggressively quantized model.

“The model is extremely slow”

This can happen when:

• your CPU is doing most of the work
• the model is too large
• the model is partially offloaded
• your system has insufficient RAM/VRAM
• context size is too large

Solution: Try a smaller model and check whether GPU acceleration is being used.

“The model loads but crashes”

Possible causes include insufficient memory, incompatible runtimes or problematic model files.

Solution: Try another model version or reduce the model/context requirements.

“My storage is disappearing”

AI models can be surprisingly large.

Solution: Check your model directory and remove models you no longer use.

Ollama vs LM Studio: Which Should You Use?

For beginners, the distinction is simple:

Choose LM Studio if you prefer a graphical interface and want to download, configure and chat with models through an app.

Choose Ollama if you prefer a command-line workflow or want to integrate local models into development tools and applications.

They can also complement each other rather than being mutually exclusive.

LM Studio provides local APIs and developer tooling, while Ollama provides a local API and command-line workflow.

Is Running AI Locally Worth It?

For the right use case, yes.

Local AI makes particular sense if you want to:

• experiment with LLMs
• learn how AI inference works
• build AI applications
• process some documents locally
• reduce dependence on cloud services
• work without an internet connection
• create a private AI development environment
• experiment with AI agents and local APIs

But don’t buy expensive hardware solely because a model has a large parameter count.

Start with your actual workload.

A smaller model that runs smoothly on your existing computer can be a much better starting point than a huge model that overwhelms your hardware.

Frequently Asked Questions

What is the easiest way to run an AI model locally?

For beginners, LM Studio provides a graphical interface for downloading and running local LLMs, while Ollama provides a straightforward command-line approach.

Can I run ChatGPT locally?

You cannot simply download the proprietary ChatGPT model and run it on your PC. Instead, you can run compatible downloadable/open-weight models such as Llama, Qwen, Mistral, Gemma and others using local AI software.

Can I run AI locally without the internet?

Yes. After downloading the necessary application, model and runtime components, compatible local AI software can operate offline.

How much RAM do I need for local AI?

There is no universal number. 16GB is a useful starting point, while 32GB or more gives you greater flexibility for larger models and multitasking. LM Studio currently recommends at least 16GB RAM on Windows.

Do I need an NVIDIA GPU?

No. Local AI can run using CPUs, integrated graphics or other supported GPU hardware. However, supported dedicated GPUs can significantly improve performance for suitable workloads.

Can I run AI models on a normal laptop?

Yes. Many laptops can run smaller local models. The important factors are RAM, processor, GPU/VRAM, cooling and storage.

Is local AI completely private?

Local processing can provide strong privacy advantages because prompts and documents can remain on your device. However, you should still check the application’s network behavior, integrations, telemetry and the model’s license before assuming that everything is completely private.

Final Thoughts

Running AI models locally on a PC is no longer limited to researchers or AI developers with expensive workstations.

With tools such as Ollama and LM Studio, beginners can download a compatible model, load it into memory and start experimenting with AI directly on their own computer.

The most important lesson is simple:

Choose the model according to your hardware—not the other way around.

Start with a smaller model, learn how local inference works, and gradually experiment with larger models as your hardware allows.

Whether you’re interested in private AI, offline chatbots, AI coding assistants, document analysis, RAG or AI development, local models provide an excellent way to learn and experiment without depending entirely on cloud-based AI services.