Your task is to develop a solution optimized to run on edge devices, leveraging lightweight models such as Llama 3.2 1B or 3B for real-time operations on mobile or IoT platforms. However, you have two options to incorporate larger models in the process:

a. Network-Enhanced Features: You can integrate network features into your solution that allow it to tap into larger models, such as Llama 3.1 405B or Llama 3.2 multimodal models (11B, 90B), running on external servers. These can be used for advanced tasks like complex data analysis or multimodal processing, adding powerful AI capabilities without overloading the edge devices.

b. Development Optimization: Alternatively, during development, you can use the larger models for purposes like synthetic data generation (using Llama 3.1 405B) or labeling data with vision models (using Llama 3.2). This approach allows you to train or fine-tune the smaller, lightweight models for your final solution, ensuring that the end product runs entirely on edge devices while benefiting from the advanced capabilities of the larger models during its creation.

The final goal is to produce an edge-ready solution that intelligently combines local performance with the optional power of larger models via network or development-stage optimization