Run GPT-Level Models on Your Laptop
Ollama lets you run open-source LLMs (Llama 3, Mistral, Gemma, Phi) locally with a simple API. Free forever — no API keys, no rate limits, no cloud.
Setup
# Install
curl -fsSL https://ollama.com/install.sh | sh
# Pull a model
ollama pull llama3.2 # 2GB, runs on most laptops
ollama pull mistral # 4GB, great for coding
ollama pull phi3 # 1.7GB, fastest
API (OpenAI-Compatible)
import requests
def chat(prompt, model="llama3.2"):
r = requests.post("http://localhost:11434/api/generate",
json={"model": model, "prompt": prompt, "stream": False})
return r.json()["response"]
print(chat("Write a Python function to check if a number is prime"))
Chat Conversations
def chat_conversation(messages, model="llama3.2"):
r = requests.post("http://localhost:11434/api/chat",
json={"model": model, "messages": messages, "stream": False})
return r.json()["message"]["content"]
response = chat_conversation([
{"role": "system", "content": "You are a Python expert."},
{"role": "user", "content": "How do I read a CSV file?"}
])
print(response)
Use With OpenAI SDK
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
response = client.chat.completions.create(
model="llama3.2",
messages=[{"role": "user", "content": "Explain Docker in one paragraph"}]
)
print(response.choices[0].message.content)
Switch from OpenAI to local LLM by changing one line.
List Models
def list_models():
r = requests.get("http://localhost:11434/api/tags")
return [{"name": m["name"], "size": m["size"] // (1024**3)}
for m in r.json()["models"]]
for m in list_models():
print(f"{m[name]}: {m[size]}GB")
Ollama vs Cloud LLMs
| Feature | Ollama | OpenAI | Claude |
|---|---|---|---|
| Cost | $0 | $0.01-0.06/1K | $0.01-0.08/1K |
| Privacy | 100% local | Cloud | Cloud |
| Speed | Depends on GPU | Fast | Fast |
| Internet | Not needed | Required | Required |
| Models | Open source | Proprietary | Proprietary |
Ollama for privacy and cost. Cloud for quality and speed.
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