🔥Llama.cpp on Google Collab: Setup in 2 Minutes

Llama.cpp Setup on Google Collab is one of the fastest ways to test and experiment with GGUF models without worrying about local machine limitations. If you want a quick, reproducible environment to validate GGUF models, Google Collab combined with llama.cpp is a powerful and beginner-friendly option.

In this guide, we’ll walk through a minimal yet practical Llama.cpp Setup on Google Collab, explain how the code works, and show you how to find compatible GGUF models on Hugging Face.

🚀 Why Use Llama.cpp Setup on Google Collab?

Using Llama.cpp Setup on Google Collab gives you:

• Zero local setup effort
• Easy CPU-based testing of GGUF models
• A fast way to validate prompts and outputs
• Reproducible notebooks for experiments

Google Collab is especially useful when you want to quickly test llama.cpp behavior, model compatibility, or prompt formatting before moving to local or production environments.

⚙️ Step-by-Step Llama.cpp Setup on Google Collab

1️⃣ Install llama.cpp Python Bindings

!pip install -U llama-cpp-python --no-cache-dir

This installs the official Python bindings for llama.cpp, enabling GGUF model inference directly in Python.

2️⃣ Download a GGUF Model in Google Collab

import os
import requests
from llama_cpp import Llama

MODEL_URL = "https://huggingface.co/MaziyarPanahi/gemma-3-1b-it-GGUF/resolve/main/gemma-3-1b-it.Q2_K.gguf?download=true"

model_dir = "models"
model_path = os.path.join(model_dir, "target_model.gguf")

os.makedirs(model_dir, exist_ok=True)

def download_model(url, dest_path):
    print("Downloading model...")
    with requests.get(url, stream=True) as r:
        r.raise_for_status()
        with open(dest_path, "wb") as f:
            for chunk in r.iter_content(chunk_size=1024 * 1024):
                if chunk:
                    f.write(chunk)
    print("Download complete")

if not os.path.exists(model_path):
    download_model(MODEL_URL, model_path)

print("Exists:", os.path.exists(model_path))
print("Size (MB):", os.path.getsize(model_path) / (1024 * 1024))

This ensures a streamed download, which is essential for large GGUF files in Google Collab.

3️⃣ Initialize the Model Using Llama.cpp Setup

llm = Llama(
    model_path=model_path,   # Path to the GGUF model file loaded by llama.cpp

    n_ctx=2048,              # Maximum number of tokens the model can process at once (context window)

    n_threads=4,             # Number of CPU threads used for inference

    n_batch=256,             # Number of tokens processed in parallel during prompt ingestion

    verbose=False            # Disable detailed internal llama.cpp logs
)

This configuration is ideal for Google Collab CPU environments, balancing speed and memory usage.

4️⃣ Run a Test Chat Completion

response = llm.create_chat_completion(
    messages=[
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ]
)

print(response["choices"][0]["message"]["content"])

🔍 How to Find GGUF-Supported Models on Hugging Face

https://huggingface.co/models?num_parameters=min:0,max:6B&library=gguf&sort=trending

This URL will show you all the models which are supported in GGUF format. Open any model page, for example, lets got with gemma.

https://huggingface.co/MaziyarPanahi/gemma-3-4b-it-GGUF

Go to the Files and Versions tab:

https://huggingface.co/MaziyarPanahi/gemma-3-4b-it-GGUF/tree/main

Pick the .gguf file from the repository based on your requirement > right click on the download icon > copy link address.

Now use this link and replace the “MODEL_URL” in the code above.

⚠️ Performance Note for Google Collab

Even with quantized models, Google Collab CPU inference is slow. A 1B–3B GGUF model may still take 20–30 seconds per response. This is expected and not an issue with your Llama.cpp Setup.

📌 When Should You Use This Setup?

• Testing GGUF compatibility
• Prompt experimentation
• Learning llama.cpp APIs
• Rapid prototyping

For production or real-time chat, a local machine or GPU-backed VM is recommended.

📄 Working .ipnyb file

https://drive.google.com/file/d/1fEsfIIOdMFWyY7mYnoooP98UepKrvj1d/view?usp=sharing

👨🏻‍💻 Frequently Asked Questions (FAQs)

❓ What is Llama.cpp?

Llama.cpp is a lightweight C++ inference engine that allows you to run large language models locally using CPU or GPU, primarily with models in the GGUF format.

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❓ What is Google Collab and why use it with Llama.cpp?

Google Collab provides a free, cloud-based Jupyter notebook environment that makes it easy to test Llama.cpp setups without installing dependencies on your local machine.

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❓ Which model formats are supported by Llama.cpp?

Llama.cpp only supports models in the GGUF format. Models in formats like .safetensors or .bin must be converted before use.

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❓ Do quantized models improve performance?

Quantized models reduce memory usage and make models easier to load, but they do not significantly reduce CPU computation time during inference.