Ai-Infrastructure

Table of Contents Introduction Understanding LLMs and Cloud Infrastructure Google Cloud’s LLM Ecosystem Core GCP Services for LLM Deployment On-Device LLM Inference Private LLM Deployment on GCP High-Performance LLM Serving with GKE Building LLM Applications on Google Workspace Best Practices for LLM Operations Resources and Further Learning Introduction Large Language Models (LLMs) have revolutionized artificial intelligence and are now integral to modern application development. However, deploying and managing LLMs at scale presents significant technical challenges. Google Cloud Platform (GCP) offers a comprehensive suite of tools and services specifically designed to address these challenges, from development and training to production deployment and monitoring. ...

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...