💰 AI Compute Card TCO Calculator
Estimate the Total Cost of Ownership of AI compute cards in real time — purchase, electricity, data center, and cooling in one calculator, with multi-chip side-by-side comparison and a "self-build vs rent" decision aid, so your procurement choices are backed by data.
⚙️ Advanced (idle power / PUE / discounting)
Select a chip above to start
0 AI accelerators supported📖 Methodology
In single-node / bare card mode, TCO consists of four parts:
TCO = Purchase cost + Electricity cost + Data center rent + Cooling cost
1. Purchase cost = unit price × quantity
2. Electricity cost = TDP(kW) × quantity × utilization × electricity rate($/kWh) × 8760 hours/year × years of use
(actual power is linearly interpolated as idle share + (1 − idle share) × utilization; idle defaults to 15% of TDP)
3. Data center rent = annual rent per card × quantity × years of use
4. Cooling cost = electricity cost × (PUE − 1)
(PUE defaults to 1.3, i.e. cooling is roughly 30% of equipment electricity; liquid cooling can be as low as 1.1)
Future cash flows are discounted to present value at 8%/year; the purchase cost is a one-time outlay and is not discounted. All parameters can be adjusted in real time in the tool above.
Switching to cluster-level deployment mode adds the following on top of the bare-card costs:
5. Server nodes (one-time) = unit server price × ⌈quantity ÷ 8⌉ (each node hosts 8 cards)
6. Network equipment (one-time) = GPU purchase cost × network share (default 12%)
7. Staff OPEX (discounted) = quantity ÷ 1000 × annual staff cost per 1,000 cards
It also outputs the break-even utilization for "self-build vs cloud rental", and the annualized TCO per TFLOPS (a compute-normalized cost metric for easier cross-chip comparison).
📊 Historical comparison examples (click to expand; values recomputed with the current model and pricing.json)
Example 1: NVIDIA B200 vs AMD MI300X (8-card server, 3 years)
All computed with default parameters: 8 cards · 90% utilization · $0.10/kWh · $500 annual rent per card · 15% idle power · PUE 1.3 · 8% discount rate · single-node mode.
| Cost Item | NVIDIA B200 | AMD MI300X | Difference |
|---|---|---|---|
| Purchase cost ($42,000 vs $13,500/card) | $336,000 | $108,000 | B200 costs $228,000 more |
| Electricity cost (discounted) | $16,525 | $12,394 | B200 costs $4,131 more |
| Data center rent (discounted) | $10,308 | $10,308 | Same |
| Cooling cost (discounted) | $4,958 | $3,718 | B200 costs $1,239 more |
| Total TCO | $367,791 | $134,420 | B200 costs $233,371 more |
Conclusion: the B200's TCO is 2.7× that of the MI300X ($367,791 vs $134,420), but the two target completely different segments — the B200 is a flagship training card (FP4 20 PFLOPS sparse, 8 TB/s memory bandwidth), while the MI300X focuses on large-memory inference (192 GB HBM3, 5.3 TB/s bandwidth).
A fairer angle: normalized by the FP16 compute field used by the calculator, the B200's "annualized TCO per TFLOPS" is about $3.06 and the MI300X's about $4.29 — per unit of compute, the B200 is actually about 29% cheaper. (The B200 uses 5,000 TFLOPS sparse and the MI300X 1,307 TFLOPS dense; figures across different bases cannot be compared directly as performance — this is for cost normalization only.) So:
- For per-card throughput / large-scale training: the B200 has the better per-compute cost;
- On a tight budget, or for large-memory inference (192 GB HBM3): the MI300X has a far lower absolute entry cost.
Example 2: Impact of electricity rates on TCO (NVIDIA B200 × 8, 3 years)
Other parameters identical to Example 1; only the electricity rate changes.
| Electricity rate ($/kWh) | Electricity cost (discounted) | Total TCO | Electricity as % of purchase cost |
|---|---|---|---|
| $0.05 | $8,263 | $357,050 | 2.5% |
| $0.10 | $16,525 | $367,791 | 4.9% |
| $0.15 | $24,788 | $378,532 | 7.4% |
| $0.20 | $33,050 | $389,274 | 9.8% |
Conclusion: each doubling of the electricity rate doubles the discounted electricity cost, but its impact on total TCO is relatively mild — under the default parameters electricity is only about 4.5% of TCO, and purchase cost remains the dominant share. What the electricity rate really amplifies is energy-efficiency differences: the higher the rate, the more pronounced the disadvantage of high-power chips versus low-power ones.
When does electricity become the leading cost? Push the rate to $0.40/kWh and electricity can reach about 15% of TCO; extend the service life to 8 years and it can reach about 9%. Only then do the gains from "choosing efficiency" become substantial.
💡 How to Lower TCO?
1. Choose more energy-efficient chips
Energy efficiency reference table (click to expand)
| Chip | TDP (W) | FP8 compute (TFLOPS) | FP8 efficiency (TFLOPS/W) | FP4 compute (TFLOPS) | FP4 efficiency (TFLOPS/W) |
|---|---|---|---|---|---|
| NVIDIA H200 | 700 | 3,958 (sparse) | 5.7 | — | — |
| NVIDIA B200 | 1,000 | 10,000 (sparse) | 10.0 | 20,000 (sparse) | 20.0 |
| AMD MI300X | 750 | 2,614 (dense) | 3.5 | — | — |
| AMD MI355X | 1,400 | 5,000 (dense) | 3.6 | 10,100 (sparse) | 7.2 |
| Huawei Ascend 950 | 500 | — | — | 2,000 | 4.0 |
Note: efficiency = rated compute ÷ TDP. In 2026, new cards' headline metric has shifted from FP8 to FP4 (4-bit floating point, trading precision for throughput). All figures are vendor-rated; sparse / dense bases are noted in parentheses — cross-vendor comparisons must not ignore base differences. Data follows the chip cards.
The higher the energy efficiency, the lower the long-term TCO.
2. Increase utilization
- Taking the MI300X from Example 1: raising utilization from 50% to 90% only lifts TCO from $128,433 to $134,420 (+4.7%), while compute output rises 80%.
- Use virtualization, multi-tenancy, and similar techniques to raise GPU utilization.
3. Choose regions with lower electricity rates
Regional electricity rate reference (click to expand)
| Region | Industrial rate ($/kWh) | 3-year electricity (B200 × 8, discounted) | vs China |
|---|---|---|---|
| Middle East | $0.04 | $6,610 | Saves $6,610 |
| China | $0.08 | $13,220 | Baseline |
| US | $0.12 | $19,830 | Costs $6,610 more |
| Europe | $0.20 | $33,050 | Costs $19,830 more |
4. Use liquid cooling
- Liquid cooling saves 20-30% of energy versus air cooling
- The initial investment is higher, but long-term TCO is lower.
⚠️ Disclaimer
- The TCO estimates provided by this calculator are for reference only; actual costs may vary due to many factors.
- Pricing data comes from public information (official guide prices, market averages, etc.); actual purchase prices may differ significantly.
- Operating costs such as electricity and data center rent vary by region, vendor, negotiating leverage, and other factors.
- Factors such as depreciation, residual value, maintenance costs, and software licensing costs are not included.
- Before any actual purchase, obtain detailed quotes from vendors.
📚 Further Reading
- Full AI Compute Card Comparison Table - Side-by-side specs for 230+ chips
- Future Roadmap - 2025-2026 new product release timeline
- Industry News - Latest launches, acquisitions, IPOs, and deep analysis