How to Serve Gemma Models on L40S GPUs with Docker

Google's Gemma family of models are lightweight, open-source large language models derived from the same research as Google's Gemini models, available in sizes like 2B, 9B, and 27B parameters. The largest, Gemma 27B, is resource-intensive and can be efficiently deployed using Docker on an NVIDIA L40S GPU, which offers 48 GB of VRAM, making it suitable for running these models without requiring multiple GPUs. Deploying Gemma models involves setting up a Docker environment, either using Hugging Face's Text Generation Inference server for a quick setup or creating a custom server for more control. The L40S is a cost-effective choice for short-term workloads, priced at about $0.79 per hour on Runpod, and provides a powerful single-GPU solution for handling the memory demands of the 27B model. Docker ensures consistent and portable deployment environments, easing the management of complex ML libraries and large models. Users should consider the balance between model size, accuracy, and cost, and be mindful of potential licensing requirements when using Gemma models in commercial applications.