Free and Fast LLMs with Groq®

November 2024•5 min read

In the rapidly evolving landscape of AI, speed and cost are crucial factors for developers. Groq's LPU™ (Language Processing Unit) technology is revolutionizing how we interact with large language models, offering blazingly fast inference times at no cost through their public API.

Why Groq?

Speed: Response times as low as 1-2ms per token
Free Tier: Generous API access at no cost
Model Support: Access to Mixtral, Llama, and more

Getting Started

Let's implement a simple chat interface using Groq's API. First, you'll need to:

Sign up for a free account at console.groq.com
Generate an API key
Install the Groq Python package

pip install groq

Implementation Example

import os
from groq import Groq

# Initialize the Groq client
client = Groq(
    api_key=os.environ["GROQ_API_KEY"],
)

def generate_response(prompt: str):
    # Create a chat completion
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="mixtral-8x7b-32768",
        temperature=0.7,
        max_tokens=1024,
        stream=True,  # Enable streaming
    )

    # Process the streaming response
    for chunk in chat_completion:
        # Print each chunk as it arrives
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].delta.content, end="")

# Example usage
prompt = "Explain the benefits of using Groqs LPU technology"
generate_response(prompt)

Performance Comparison

Groq LPU™

1-2ms/token

Traditional GPU

50-100ms/token

Best Practices

Use streaming for real-time responses
Implement proper error handling
Monitor rate limits
Cache responses when appropriate

Get Started with Groq