Building a Multimodal Local AI Stack: Gemma 4 E2B, vLLM, and Hermes Agent

The Local AI movement just hit a massive milestone. With the release of Google's Gemma 4, 2-billion parameter models are no longer toys for simple chat. They're multimodal powerhouses purpose-built for advanced reasoning and agentic workflows. In this guide, we'll break down how to harness the Gemma 4 E2B (Effective 2B) model using vLLM and integrate it with the Hermes Agent for a fully local, multimodal stack. What is Gemma 4? Google released Gemma 4 in four sizes: Effective 2B (E2B), Effective 4B (E4B), 26B Mixture of Experts, and 31B Dense. We're focused on the E2B; the one that fits on consumer hardware. Key capabilities: Multimodal from day one - all models natively process text, images, and video. The E2B and E4B edge models also support audio input for speech recognition. Long context - edge models like E2B feature a 128K context window. Apache 2.0 licensed - commercially permissive, no strings attached. Why E2B + vLLM for a local agent stack? Instruction tuning — Gemma 4 excels