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AI Compute Cost Calculator

Data Centers

Ground AI training & inference costs in real commodity prices

Configuration

70B params
7B2T
1 month
1 week6 months
1.30
1.102.00
80%
50%95%

GPU Specifications

TDP
700W
Memory
80 GB
FP16 TFLOPS
990
Unit Price
$30,000
GPUs required: 2 — Fleet cost: $60.0K

GPU pricing last verified: 2026-03-15. Actual pricing varies by volume and configuration.

Training Cost
$2.7K
1 month
$/M Tokens
$2.51
inference
Power
0.0 MW
total draw
Electricity
$869.86
/year
Water
6.3K gal
/year
Carbon
5.6 t
CO2/year

Training Cost Breakdown

GPU Amortization$2.3K86.3%
Electricity$66.732.5%
Networking$184.116.9%
Facility$115.074.3%

Sensitivity Analysis

PUE Impact on Training Cost
1.1
$2.7K
1.2
$2.7K
1.3
$2.7K
1.4
$2.7K
1.6
$2.7K
2.0
$2.7K
Utilization Impact
50%
$2.6K
60%
$2.7K
70%
$2.7K
80%
$2.7K
90%
$2.7K
95%
$2.7K
Deployment Model Impact
Owned
$1.8K
Colo
$2.7K
Cloud
$5.3K
Owned inf.$1.93/M tok
Colo inf.$2.51/M tok
Cloud inf.$3.20/M tok

Market Comparison — Same Config, Different Markets

MarketTraining CostElectricity$/M Tokens
N. Virginia$2.7K$66.73$2.51
DFW$2.6K$48.43$1.84
Phoenix$2.7K$55.97$2.11
C. Ohio$2.7K$51.66$1.95
Atlanta$2.7K$53.81$2.03
Chicago$2.7K$59.20$2.23
SV$2.7K$80.72$3.02
Portland / Hillsboro$2.6K$40.90$1.56
Denver / Aurora$2.6K$45.20$1.72
Reno / Las Vegas$2.6K$43.05$1.64

Methodology

GPU count estimated as ceil(modelParams * 2 / gpuMemory) for bf16 training. Training cost = GPU amortization (3-year) + electricity + networking (8%) + facility (5%). Cloud pricing uses a 3x multiplier over owned hardware; colocation uses 1.5x.

Inference cost per million tokens derived from GPU throughput (FP16 TFLOPS / 2 * modelParams) plus power cost at market rates. Water usage from DOE/industry data by market. Carbon at 0.4 tCO2/MWh (US grid average).

Electricity prices from CBRE Q3 2025 DC Market Report, EIA-861, and ISO LMP data. All figures are estimates for comparison purposes.