Hyperscale Data (GPUS) Interest Expenses (2012 - 2026)
Hyperscale Data's Interest Expenses was $3.94 million in Q2 2026, down 48.6% from $7.66 million a year earlier and down 39.8% from the prior quarter.
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Analysis
Hyperscale Data (GPUS) Interest Expenses (2012 - 2026) Analysis & Trends
On a trailing twelve-month basis, Hyperscale Data's Interest Expenses was $15.11 million through Jun 30, 2026, down 28.4% year-over-year; for FY2025, it came in at $16.12 million, down 22.0% from FY2024.
- Interest Expenses shows a five-year compound annual growth rate of 10.8% (FY2020 to FY2025).
- In earlier years, Interest Expenses was $20.66 million in FY2024 (-53.4%), $44.31 million in FY2023 (+18.7%), $37.34 million in FY2022 and $1.87 million in FY2021 (-80.6%).
- Quarterly Interest Expenses has moved between $140,000 (Q3 2021) and $29.82 million (Q1 2022) over five years.
- Compared with a year earlier, Interest Expenses was higher in three of the last eight quarters, with an average decline of 11.9%.
- The best year-over-year quarter for Interest Expenses over five years was Q2 2023 (growth of 684.2%); the worst was Q3 2021 (a decline of 94.1%).
- Per Business Quant data, GPUS's Interest Expenses in the three quarters before Q2 2026 was $6.55 million (Q1 2026), $1.55 million (Q4 2025) and $3.06 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Int Expense (Qtr) |
|---|---|---|---|---|---|
| 1 | PayPal Holdings | 50.08 Bn | 8.30 Bn | - | - |
| 2 | Block | 46.71 Bn | 18.00 Bn | 3.17 Bn | - |
| 3 | Coinbase Global | 45.00 Bn | 1.80 Bn | - | 22.52 Mn |
| 4 | Corpay | 26.84 Bn | 16.72 Bn | - | 114.72 Mn |
| 5 | Fiserv | 25.68 Bn | 22.36 Bn | 2.90 Bn | 376.00 Mn |
| 6 | Global Payments | 22.94 Bn | 732.47 Mn | 2.03 Bn | 277.54 Mn |
| 7 | Guidewire Software | 15.52 Bn | 12.08 Bn | 269.68 Mn | 3.36 Mn |
| 8 | Bitmine Immersion Technologies | 12.62 Bn | 10.00 Bn | 40.81 Mn | - |
| 9 | Chime Financial | 11.56 Bn | 7.35 Bn | 594.87 Mn | - |
| 10 | Hyperscale Data | 67.58 Mn | -130.73 Mn | 8.80 Mn | 3.94 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 3.94 Mn |
| Mar 31, 2026 | 6.55 Mn |
| Dec 31, 2025 | 1.55 Mn |
| Sep 30, 2025 | 3.06 Mn |
| Jun 30, 2025 | 7.66 Mn |
| Mar 31, 2025 | 3.84 Mn |
| Dec 31, 2024 | 1.84 Mn |
| Sep 30, 2024 | 7.77 Mn |
| Jun 30, 2024 | 5.32 Mn |
| Mar 31, 2024 | 5.63 Mn |
| Dec 31, 2023 | 9.13 Mn |
| Sep 30, 2023 | 6.14 Mn |
| Jun 30, 2023 | 15.93 Mn |
| Mar 31, 2023 | 12.10 Mn |
| Dec 31, 2022 | 5.28 Mn |
| Sep 30, 2022 | 2.37 Mn |
| Jun 30, 2022 | 2.03 Mn |
| Mar 31, 2022 | 29.82 Mn |
| Dec 31, 2021 | 1.40 Mn |
| Sep 30, 2021 | 140,000.00 |
API Access
Hyperscale Data Interest Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=interest-expenses&ticker=GPUS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "interest-expenses", "ticker": "GPUS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=interest-expenses&ticker=GPUS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();