Self host Gemma 4: Deploy LLMs on Cloud Run GPUs

GCP credit → https://goo.gle/handson-ep7-lab1 Lab → https://goo.gle/guardians In this episode, we deploy Google's Gemma 4 model to Cloud Run two completely different ways, each with real trade-offs you need to understand before choosing one for production. 🔨 Ollama — model baked into the container. Instant cold starts. Rebuild to update. ⚡ vLLM — model mounted from Cloud Storage via FUSE. Slower first boot, but swap models without redeploying. Both use Cloud Run GPUs, scale to zero, and ship through automated CI/CD with Cloud Build. We build both. You decide which fits. 👇 📦 CI/CD with Cloud Build 🖥️ GPU accelerated serverless inference 🔄 Baked in vs. decoupled model architecture 🚀 Scale to zero ⚖️ Cold start speed vs. production agility Chapters: 0:00 - Intro 6:08 - Getting started with Agentverse lab 7:57 - Laying the foundations of the citadel 16:07 - Forging the power core: Self hosted LLMs 28:02 - Forging the citadel's central core: Deploy vLLM 43:59 - Summary More resources: Cloud Run GPU documentation → https://goo.gle/4sEbTvG Ollama documentation → https://goo.gle/3Qdi64w vLLM documentation → https://goo.gle/4cvvxE9 Cloud Storage FUSE → https://goo.gle/4cQAb0V Watch more Hands on AI → https://www.youtube.com/watch?v=qCBreTfjFHQ&list=PLIivdWyY5sqKnJOvP89yF8t9mWuzMTcbM 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech #Gemma4 #CloudRun Speakers: Ayo Adedeji, Annie Wang Products Mentioned: Agent Development Kit, Gemini API, Cloud Run