TPU Economics: Why vLLM is Reshaping Cost-Per-Inference for Large Language Models

vLLM brings memory-efficient LLM inference to Google Cloud TPUs with first-class support for v5e, v6e (Trillium), and Ironwood chips. For high-volume serving workloads, Trillium's 4.7x compute advantage and 67% energy efficiency gain over v5e make TPUs compelling alternatives to GPU-based deployments at scale. An interactive cheat sheet helps teams right-size TPU configurations and calculate exact serving costs for popular models like Gemma and Llama.