The Developer's Guide to Running LLMs Locally: Ollama, Gemma 4, and Why Your Side Projects Don't Need an API Key

The approach outlined in the article by Nrk Raju Guthikonda is a practical and efficient way to leverage local large language models (LLMs) for various applications without relying on cloud-based services. Here's a summary of key points and steps you can follow:

Key Points

1. Local Deployment: Use Docker Compose to deploy Ollama, which hosts the LLMs locally.
2. Model Selection: Utilize pre-trained models like Gemma 4 for tasks requiring less computational power but still offering high-quality responses.
3. Development Environment:
   • Python Framework: Develop your application using Python and libraries such as FastAPI for REST APIs and Streamlit for web interfaces.
   • Dockerfile: Create a Dockerfile to build an image that includes all necessary dependencies and configurations.

Steps to Follow

1. Set Up Local Environment

• Install Docker on your machine (ensure it supports GPU if you plan to use hardware acceleration).
• Clone the repository or set up your project directory with the necessary files (Docker Compose, Python scripts, etc.).

yaml
 
1# docker-compose.yml
2services:
3  ollama:
4    image: ollama/ollama:
5
6[Read the full article at DEV Community](https://dev.to/kennedyraju55/the-developers-guide-to-running-llms-locally-ollama-gemma-4-and-why-your-side-projects-dont-54oe)
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