Built a Data Gravity calculator for AI infrastructure placement — here's why egress cost is the variable most GPU comparisons ignore

Most GPU provider comparisons compare hourly rates and stop there. That's the wrong model for anything beyond a toy workload.

The variable that actually determines placement economics is data gravity — the friction cost of moving your dataset to each provider.

I built a calculator that makes this concrete. Here's the methodology:

The Gravity Score

G = (Dataset Size in GB × Egress Rate) ÷ Monthly Compute Cost

• G > 0.5: egress exceeds 50% of compute cost — migration is economically irrational
• G < 0.1: data is effectively weightless — cheapest compute wins
• Between 0.1 and 0.5: the actual decision space

At 50TB, here's what the scores look like:

• AWS: 19.6% (egress $0.09/GB)
• GCP: 34.2% (egress $0.12/GB)
• CoreWeave: 1.4% (egress $0.01/GB — near zero)
• Lambda: 0.0%* (no published fee, bandwidth caps apply)
• Nutanix/Cisco on-prem: N/A — co-located

Why BF16 as the benchmark (not INT4)

Llama 3 70B at BF16 requires ~145GB VRAM just for weights — forcing a multi-GPU configuration. This reveals which providers have the InfiniBand/NVLink fabric needed to bridge GPUs without latency penalties. INT4 fits on one GPU. BF16 tells you what production actually costs.

Normalized provider table (April 2026, $/GPU-hr at 8-GPU BF16 config):

• Lambda: $2.99 — cheapest, but bandwidth caps at scale
• Nutanix AHV: $2.15 (36-mo CapEx amort + 20% OpEx adder)
• Cisco UCS M7: $2.45 (same)
• AWS p5: $3.93
• GCP A3: $3.00
• CoreWeave HGX: $6.16 — highest, but dedicated InfiniBand, no multi-tenant contention

The counterintuitive result: at 50TB steady-state inference (100% duty cycle), Nutanix AHV wins at $125.56/1M tokens vs $274.51 on AWS — a 54% reduction. Below 70% duty cycle, the crossover flips toward cloud.

Happy to discuss the methodology — particularly the OpEx adder assumptions for on-prem (defaulting to 20%, adjustable to 35% for older facilities).

Happy to share the link if anyone wants to run their own numbers.