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Run LLaMA 3 Locally and Build AI Apps 🤖💻


This content originally appeared on DEV Community and was authored by Kamrul Arafin

Introduction

So you’ve seen everyone flexing their ChatGPT or Claude bots, but here’s the real kicker: you can now run LLaMA 3—Meta’s latest large language model—right on your laptop. No crazy cloud bills, no throttled APIs, just raw local AI power at your fingertips.

Why does this matter? Because developers are no longer tied to vendor APIs. Local LLMs = control, privacy, and cost savings. Plus, it’s just cool to say “Yeah, my laptop runs a 70B parameter model”.

Step 1: Install Ollama (Easiest Way)

The smoothest route is using Ollama, a CLI tool for running and managing open-source LLMs.

# Install Ollama (Mac/Linux)
curl -fsSL https://ollama.com/install.sh | sh

# Run LLaMA 3
ollama run llama3

Boom 💥—you’ve got LLaMA chatting locally.

Step 2: Chat With the Model

Once installed, you can open an interactive session:

ollama run llama3
> What’s the difference between Python lists and tuples?

Output: A neatly explained answer with examples, just like you’d expect from ChatGPT.

Step 3: Build AI-Powered Apps With Node.js / Python

Here’s the fun part—hooking it into your code.

Node.js Example:

import ollama from "ollama";

const response = await ollama.chat({
  model: "llama3",
  messages: [{ role: "user", content: "Write a haiku about debugging." }],
});

console.log(response.message.content);

Python Example:

from ollama import Client

client = Client()
response = client.chat(
    model="llama3",
    messages=[{"role": "user", "content": "Explain Docker to a 5-year-old"}]
)
print(response['message']['content'])

Step 4: Extend With Tools (Embeddings, RAG, Agents)

Local models aren’t just for chatting. You can:

  • Generate embeddings for semantic search.
  • Hook into vector databases like Pinecone or Weaviate.
  • Build RAG apps with LangChain.
  • Experiment with agents that can call APIs, browse docs, or even control your computer.

Why Go Local?

  • 💸 Save money – no $500 OpenAI bill.
  • 🔒 Privacy – your data stays on your machine.
  • ⚡ Speed – avoid API rate limits.
  • 🛠 Hackability – fine-tune and customize models as you wish.

Conclusion

Running LLaMA 3 locally is a game-changer for developers. It gives you independence from API providers, lets you experiment freely, and opens doors to custom AI apps without breaking the bank.

👉 If you’ve been waiting for the right moment to dive into local AI—this is it.

Bookmark this post, try Ollama today, and share what you build. The local AI revolution is just getting started!


This content originally appeared on DEV Community and was authored by Kamrul Arafin