What are GCP's options for running AI inference at scale?

GCP provides multiple tiers for AI inference. Vertex AI Predictions: deploy models as managed online endpoints (REST API) with automatic scaling, A/B testing (traffic split between model versions), and monitoring. Supports TensorFlow, PyTorch, scikit-learn, and custom containers. Vertex AI Batch Predictions: process large offline datasets without a running endpoint. Cloud Run / GKE: package model as a container and deploy for full control over the serving infrastructure. Use GPU node pools in GKE for accelerated inference. Vertex AI Model Garden: access pre-built models (Gemini, Imagen, Codey) via managed endpoints. TPUs (Tensor Processing Units): Google's custom AI accelerators — significantly faster and cheaper than GPUs for training and inference of large transformer models. Available as Cloud TPUs (v4, v5e) and as the underlying hardware for Vertex AI. For the largest LLMs, Google's TPU pods provide the most cost-effective inference option globally.