Globalfoundries (GFS) Operating Expenses (2020 - 2026)
Globalfoundries' Operating Expenses was $331 million in Q2 2026, up 56.1% from $212 million a year earlier and up 22.1% from the prior quarter.
Powered by 
Analysis
Globalfoundries (GFS) Operating Expenses (2020 - 2026) Analysis & Trends
On a trailing twelve-month basis, Globalfoundries' Operating Expenses was $1.08 billion through Jun 30, 2026, down 39.9% year-over-year; for FY2025, it was $893 million, down 52.1% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of -0.6% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $1.87 billion in FY2024 (+91.9%), $972 million in FY2023 (-9.3%), $1.07 billion in FY2022 (-0.1%) and $1.07 billion in FY2021 (+16.5%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses since Q4 2024.
- Compared with a year earlier, Operating Expenses was higher in three of the last eight quarters, with growth averaging 47.9%.
- The best year-over-year quarter for Operating Expenses over five years was Q4 2024 (growth of 418.0%); the worst was Q4 2025 (a decline of 78.0%).
- Per Business Quant data, GFS's Operating Expenses in the three quarters before Q2 2026 was $271 million (Q1 2026), $253 million (Q4 2025) and $224 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,226.98 Bn | 5,001.14 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,200.79 Bn | 1,825.66 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,570.49 Bn | 1,496.54 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,017.05 Bn | 922.69 Bn | 47.05 Bn | 2.77 Bn |
| 5 | Advanced Micro Devices | 867.62 Bn | 824.37 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 616.92 Bn | 572.61 Bn | 5.90 Bn | - |
| 7 | Intel | 461.96 Bn | 331.10 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Applied Materials | 307.82 Bn | 273.26 Bn | 4.59 Bn | 1.51 Bn |
| 9 | Lam Research | 296.82 Bn | 272.87 Bn | 4.30 Bn | 1.02 Bn |
| 10 | Globalfoundries | 25.00 Bn | 13.30 Bn | 505.00 Mn | 331.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 331.00 Mn |
| Mar 31, 2026 | 271.00 Mn |
| Dec 31, 2025 | 253.00 Mn |
| Sep 30, 2025 | 224.00 Mn |
| Jun 30, 2025 | 212.00 Mn |
| Mar 31, 2025 | 204.00 Mn |
| Dec 31, 2024 | 1.15 Bn |
| Sep 30, 2024 | 229.00 Mn |
| Jun 30, 2024 | 240.00 Mn |
| Mar 31, 2024 | 246.00 Mn |
| Dec 31, 2023 | 222.00 Mn |
| Sep 30, 2023 | 268.00 Mn |
| Jun 30, 2023 | 257.00 Mn |
| Mar 31, 2023 | 225.00 Mn |
| Dec 31, 2022 | 334.00 Mn |
| Sep 30, 2022 | 253.00 Mn |
| Jun 30, 2022 | 241.00 Mn |
| Mar 31, 2022 | 244.00 Mn |
| Dec 31, 2021 | 297.00 Mn |
| Sep 30, 2021 | 248.00 Mn |
API Access
Globalfoundries 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=GFS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "GFS", "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=GFS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();