How to Run an Open-Source LLM on Your Own Computer

Running a large language model on your own computer sounds futuristic, but it is surprisingly accessible in 2026. With the right tools and realistic expectations about hardware, you can have a capable AI assistant running entirely offline - no API keys, no subscriptions, no data leaving your machine. This guide shows you how.

Why Run a Model Locally?

Before diving into the how, let us talk about the why:

Privacy. Your prompts and responses never leave your computer. No company sees your data, stores your conversations, or uses your input for training.
No recurring costs. After the initial hardware investment, every query is free. No per-token API charges, no monthly subscriptions.
Offline use. Works without an internet connection. Great for travel, restricted environments, or unreliable connectivity.
Customization. You can fine-tune models on your own data, remove safety filters for legitimate research, or modify behavior in ways API providers do not allow.
Learning. Running models locally gives you hands-on understanding of how LLMs work, which is valuable if you are building AI applications.

Hardware Requirements

Let us be realistic about what you need. The key resource is memory - either GPU VRAM or system RAM.

GPU (Recommended)

A dedicated GPU with sufficient VRAM provides the best experience:

8 GB VRAM (e.g., RTX 4060): Can run 7-8 billion parameter models comfortably. This covers models like Qwen 3 8B and Llama 3.2 8B.
16 GB VRAM (e.g., RTX 4070 Ti Super): Can run 14-20 billion parameter models. Opens up Qwen 3 14B and similar mid-range models.
24 GB VRAM (e.g., RTX 4090): Can run 30-70 billion parameter models with quantization. This is the sweet spot for enthusiasts.

NVIDIA GPUs are best supported, but AMD GPUs work with some tools. Apple Silicon Macs (M1/M2/M3/M4) are excellent for local inference - the unified memory architecture means a Mac with 32 GB of RAM can run models that would need a high-end GPU on Windows or Linux.

CPU-Only (Possible but Slower)

If you do not have a dedicated GPU, you can run models on CPU using system RAM. You need at least 16 GB of RAM for small models. The tradeoff is speed: expect roughly 5-15 tokens per second on CPU versus 30-100+ on GPU. That means responses come in noticeably slower, but it is still usable for many tasks.

Storage

Models are large files. A 7B parameter model in quantized form is about 4-5 GB. A 70B model can be 35-40 GB. Make sure you have adequate SSD storage.

The Tools

Ollama: The Easiest Way to Start

Ollama is the most beginner-friendly option. It handles downloading, managing, and running models with simple commands.

Installation:

macOS: brew install ollama or download from ollama.com
Linux: curl -fsSL https://ollama.ai/install.sh | sh
Windows: Download the installer from ollama.com

Basic usage:

# Download and run a model
ollama run qwen3:8b

# List available models
ollama list

# Pull a model without running it
ollama pull llama4-scout

# Run with a specific prompt
ollama run qwen3:8b "Explain quantum computing in simple terms"

After typing ollama run qwen3:8b, the model downloads (once) and you are dropped into an interactive chat. It really is that simple.

Ollama also exposes a local API on port 11434, which means other applications (text editors, note-taking apps, coding tools) can use your local model as a backend.

LM Studio: The Best GUI Option

If you prefer a graphical interface, LM Studio is excellent. It provides a desktop application where you can browse available models, download them with one click, and chat through a polished interface.

Getting started:

Download LM Studio from lmstudio.ai.
Open the app and browse the model catalog.
Click "Download" on any model that fits your hardware.
Switch to the Chat tab and start talking.

LM Studio also shows you performance metrics (tokens per second, memory usage) and lets you adjust generation parameters like temperature and context length. It exposes a local API compatible with the OpenAI format, so you can use it as a drop-in replacement for OpenAI's API in your applications.

llama.cpp: The Power User Option

llama.cpp is the foundational C/C++ inference engine that both Ollama and LM Studio build upon. Using it directly gives you maximum control and performance, but requires more technical comfort.

Installation:

# Clone and build
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make -j

# For GPU acceleration (NVIDIA)
make -j GGML_CUDA=1

Usage:

# Run a model
./llama-cli -m path/to/model.gguf -p "Your prompt here" -n 512

# Start an interactive chat
./llama-cli -m path/to/model.gguf --interactive-first

# Run as a server with API
./llama-server -m path/to/model.gguf --port 8080

llama.cpp is what you want if you need fine-grained control over quantization, batch processing, or integration into custom pipelines.

Which Models to Run

Here are practical recommendations based on hardware:

8 GB VRAM / 16 GB RAM:

Qwen 3 8B - Excellent all-rounder. Strong at coding, math, and general conversation.
Llama 3.2 8B - Great for general-purpose tasks with strong multilingual support.
Gemma 3 4B - Smaller but surprisingly capable for basic tasks.

16-24 GB VRAM / 32 GB RAM:

Qwen 3 32B - Outstanding quality that rivals some proprietary models.
Llama 4 Scout - Meta's latest, strong at reasoning and creative tasks.
DeepSeek R1 Distill 32B - Excellent reasoning capabilities in a manageable size.

Apple Silicon Mac (32-64 GB unified memory):

All of the above, plus larger models like Qwen 3 72B (quantized) if you have 64 GB.

Understanding Quantization

You will encounter terms like "Q4_K_M" or "Q8_0" when downloading models. This is quantization - a technique that reduces model size and memory requirements by using lower precision numbers.

Q8 (8-bit): Closest to original quality, largest file size.
Q6_K (6-bit): Nearly indistinguishable from Q8, meaningfully smaller.
Q4_K_M (4-bit): The sweet spot for most users. Good quality with significant size reduction.
Q3 and below: Noticeable quality degradation. Use only if you must fit a model into limited memory.

As a rule of thumb, start with Q4_K_M. If you have the VRAM headroom, try Q6_K for a small quality bump.

Tradeoffs to Be Aware Of

Running locally is powerful, but be honest about the limitations:

Smaller models, less capability. A local 8B model will not match Claude Opus or GPT-5 on complex reasoning. It is great for drafting, summarizing, and routine tasks, but will struggle with PhD-level questions.
Slower generation. Even with a good GPU, local models are typically slower than cloud APIs, which run on clusters of high-end hardware.
No built-in tool use. Most local setups do not support web browsing, code execution, or other agent capabilities out of the box (though frameworks like Ollama are adding these features).
You manage updates. When a new model version drops, you need to download and configure it yourself.

Getting Started Today

The fastest path from zero to a working local LLM:

Install Ollama (one command).
Run ollama run qwen3:8b (downloads the model and starts chatting).
Experiment with different prompts and see what works.

That is genuinely all it takes. From there, you can explore different models, try LM Studio for a graphical experience, or dive into llama.cpp for maximum control. Welcome to the world of local AI.