Hyperscale Data (GPUS) Operating Expenses (2010 - 2026)
Hyperscale Data (GPUS) recorded Operating Expenses of $31.91 million in Q2 2026, up 96.3% from $16.25 million a year earlier but down 12.2% from the prior quarter.
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Analysis
Hyperscale Data (GPUS) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Hyperscale Data's Operating Expenses came in at $124.17 million as of Jun 30, 2026, up 73.8% year-over-year; for FY2025, it was $83.84 million, up 3.3% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of 44.0% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $81.19 million in FY2024 (-53.4%), $174.09 million in FY2023 (-8.5%), $190.16 million in FY2022 (+305.4%) and $46.9 million in FY2021 (+246.2%).
- Quarterly Operating Expenses has ranged from $10.38 million in Q4 2024 to $118.04 million in Q4 2022 over the past five years.
- On a year-over-year basis, Operating Expenses has increased for three consecutive quarters, with growth averaging 49.5% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 631.8% in Q4 2022, against a decline of 80.4% in Q4 2024 at the low end.
- Per Business Quant, the preceding three quarters came in at $36.34 million (Q1 2026), $35.6 million (Q4 2025) and $20.32 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Coinbase Global | 50.59 Bn | 7.40 Bn | - | 1.33 Bn |
| 2 | PayPal Holdings | 46.79 Bn | 5.01 Bn | - | 7.26 Bn |
| 3 | Block | 44.05 Bn | 15.31 Bn | 3.17 Bn | 2.72 Bn |
| 4 | Iren | 31.74 Bn | 19.34 Bn | 103.89 Mn | 128.25 Mn |
| 5 | Corpay | 26.01 Bn | 16.21 Bn | - | 260.40 Mn |
| 6 | Fiserv | 24.47 Bn | 21.17 Bn | 2.90 Bn | 4.28 Bn |
| 7 | Global Payments | 22.51 Bn | 952.73 Mn | 2.03 Bn | 2.98 Bn |
| 8 | Bitmine Immersion Technologies | 15.56 Bn | 12.94 Bn | 40.81 Mn | 52.67 Mn |
| 9 | Guidewire Software | 11.90 Bn | 8.47 Bn | 269.68 Mn | 207.36 Mn |
| 10 | Hyperscale Data | 80.79 Mn | -111.20 Mn | 8.80 Mn | 31.91 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 31.91 Mn |
| Mar 31, 2026 | 36.34 Mn |
| Dec 31, 2025 | 35.60 Mn |
| Sep 30, 2025 | 20.32 Mn |
| Jun 30, 2025 | 16.25 Mn |
| Mar 31, 2025 | 11.67 Mn |
| Dec 31, 2024 | 10.38 Mn |
| Sep 30, 2024 | 33.14 Mn |
| Jun 30, 2024 | 23.14 Mn |
| Mar 31, 2024 | 14.53 Mn |
| Dec 31, 2023 | 52.90 Mn |
| Sep 30, 2023 | 29.08 Mn |
| Jun 30, 2023 | 68.39 Mn |
| Mar 31, 2023 | 32.35 Mn |
| Dec 31, 2022 | 118.04 Mn |
| Sep 30, 2022 | 25.83 Mn |
| Jun 30, 2022 | 28.72 Mn |
| Mar 31, 2022 | 21.30 Mn |
| Dec 31, 2021 | 16.13 Mn |
| Sep 30, 2021 | 13.81 Mn |
API Access
Hyperscale Data Operating 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=operating-expenses&ticker=GPUS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-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=operating-expenses&ticker=GPUS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();