Meta’s Llama models are the most widely used open-weight LLMs, and thanks to Ollama, getting one running on your own PC takes about five minutes. Here’s the complete walkthrough for installing Ollama and chatting with Llama locally, no cloud, no subscription.

Why Run Llama Locally?

A local Llama model means unlimited use with zero API costs, full privacy since nothing leaves your machine, and the ability to use it completely offline. It’s also a great way to experiment with fine-tuning and custom prompting without rate limits.

Test Config Specifications:

Monitor: MSI MAG342CQR E2 Curved Gaming Monitor
Motherboard: MSI PRO Z890-S WIFI Motherboard
CPU: Intel Core Ultra 7 265K
RAM: 32GB (2X16) Corsair Vengeance Performance DDR5 Memory
Primary SSD: Western Digital SN850 500GB PCIe Gen 4 SSD
Secondary/Game SSD: 480GB Crucial SATA SSD, WD Green 960GB SATA SSD
Power Supply: Antec HCG-1000-EXTREME PSU
GPU: INNO3D GeForce RTX 5090 X3 OC
CPU Cooler: DEEPCOOL GAMMAXX L360 ARGB
Cabinet: MSI MAG PANO 130R PZ
OS: Microsoft Windows 11 Pro

What You’ll Need

Windows 10/11, macOS, or Linux
A GPU with at least 6-8 GB VRAM for the smaller Llama models (a modern NVIDIA card like the RTX 5090 easily handles the larger ones)
10-40 GB of free disk space depending on the model size you choose

Step 1: Install Ollama

Go to ollama.com/download and download the installer for your operating system. On Windows, run OllamaSetup.exe and click Install; it takes under a minute and sets Ollama up as a background service so it’s ready whenever you need it.

How to run Llama locally with Ollama - terminal command screenshot

Step 2: Pull a Llama Model

Open a terminal and choose a size that fits your GPU:

ollama pull llama3.2:3b    # lightweight, 4GB+ VRAM
ollama pull llama3.1:8b    # balanced, 8GB+ VRAM
ollama pull llama3.1:70b   # high-end, 40GB+ VRAM

Step 3: Chat with Llama

Once downloaded, start chatting right away:

ollama run llama3.1:8b

You’ll get an interactive prompt in your terminal. Type your question, and Llama responds in real time. Use /bye to end the session. You can also connect Ollama to apps like Open WebUI for a full ChatGPT-style browser interface if you’d rather not use the terminal.

Which Llama Model Should You Pick?

Llama 3.2 (1B/3B) is fastest and ideal for lightweight laptops or quick tasks. Llama 3.1 8B is the sweet spot for most users, capable and fast on any 8GB+ GPU. Llama 3.1 70B rivals much larger commercial models but needs a high-VRAM card like the RTX 5090 (32GB) to run smoothly.

How to Run Llama Locally with Ollama: Step-by-Step Guide (2026)

Frequently Asked Questions

Is Ollama free?

Yes, Ollama and Meta’s Llama models are both free and open for personal and commercial use under Meta’s license terms.

Can I run Llama without a dedicated GPU?

Yes, on CPU with the smaller 1B or 3B models, though responses will be noticeably slower.

How much disk space does Llama need?

Roughly 2GB per billion parameters, so an 8B model needs about 5-6GB and a 70B model needs around 40GB.

Does Ollama support Llama 4?

Yes, Ollama’s model library includes Llama 4’s multimodal variants alongside Llama 3.x, pull them the same way with ollama pull llama4.

Can multiple apps use the same local Llama model?

Yes, Ollama runs as a local API server on port 11434, so any compatible app or script on your machine can send it requests once it’s running.

Run Llama Locally: Performance Tips

When you run Llama locally on Ollama, model size determines the biggest performance factor: an 8B parameter Llama model runs comfortably on 8GB of VRAM, while 70B variants need a high-end GPU like an RTX 5090 with 32GB. Quantized GGUF versions cut memory usage further, trading a little accuracy for a much smaller footprint.

Once you run Llama locally with Ollama, you can swap in other open models, like DeepSeek or Gemma, using the exact same commands. Full details on downloading and versioning are available on the official Ollama model library. If you haven’t installed Ollama yet, see our Ollama on Windows 11 setup guide first.

Most users who run Llama locally start with the 8B instruct model, since it balances speed and quality well on consumer hardware. As you get comfortable, larger context windows and bigger checkpoints become easy to try with a single command line change.

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