We Built a Data Gravity Calculator for AI Infrastructure Placement — Here's the Methodology

Most AI infrastructure decisions get made on hourly GPU rates. That's the wrong input variable. Where your data lives determines what your AI costs. A 50TB dataset sitting in S3 doesn't move to CoreWeave for free — and the cost of moving it can exceed the compute savings before you've run a single training job. We built the AI Gravity & Placement Engine to make that friction calculable before the architecture is committed. What It Does The engine calculates Token TCO for running Llama 3 70B at BF16 precision across six infrastructure tiers: AWS (p5.48xlarge — 8x H100) GCP (A3-High — 8x H100) CoreWeave HGX (bare-metal InfiniBand) Lambda H100 Nutanix AHV (H100, 36-mo CapEx amortized) Cisco UCS M7 (H100, 36-mo CapEx amortized) All providers are normalized to cost-per-GPU-hour at the 8-GPU BF16 configuration. On-prem providers use 36-month CapEx amortization plus a configurable OpEx Adder (default 20%) for power, cooling, and maintenance. Why BF16 — Not INT4 BF16 requires approximately