Lam Research (LRCX) Operating Expenses (2009 - 2026)
Lam Research's Operating Expenses was $965.25 million in fiscal Q4 2026 (quarter ended Jun 28, 2026), up 13.7% from $848.58 million a year earlier and up 11.8% from the prior quarter.
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Analysis
Lam Research (LRCX) Operating Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended Jun 28, 2026), Operating Expenses at Lam Research came in at $3.53 billion, up 14.5% from FY2025.
- Operating Expenses has now increased for seven consecutive fiscal years, with a five-year compound annual growth rate of 8.7% (FY2021 to FY2026).
- In earlier fiscal years, Operating Expenses was $3.08 billion in FY2025 (+10.4%), $2.79 billion in FY2024 (+7.2%), $2.6 billion in FY2023 (+4.5%) and $2.49 billion in FY2022 (+7.2%).
- The fiscal Q4 2026 figure marks the highest quarterly Operating Expenses in data going back to fiscal Q1 2010.
- Compared with a year earlier, Operating Expenses has increased for 11 straight quarters, with growth averaging 12.6% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was fiscal Q4 2025 (growth of 18.9%); the worst was fiscal Q4 2023 (a decline of 2.8%).
- Per Business Quant data, LRCX's Operating Expenses in the three fiscal quarters before Q4 2026 was $863.51 million (Q3 2026), $840.96 million (Q2 2026) and $855.79 million (Q1 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,503.96 Bn | 5,278.12 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,366.45 Bn | 1,992.15 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,676.58 Bn | 1,602.63 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,202.51 Bn | 1,141.27 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 998.39 Bn | 955.14 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 698.25 Bn | 654.08 Bn | 5.90 Bn | - |
| 7 | Intel | 606.32 Bn | 491.05 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 411.06 Bn | 387.85 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 405.83 Bn | 371.27 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Arm Holdings | 307.33 Bn | 293.04 Bn | 1.25 Bn | -1.16 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 28, 2026 | 965.25 Mn |
| Mar 29, 2026 | 863.51 Mn |
| Dec 28, 2025 | 840.96 Mn |
| Sep 28, 2025 | 855.79 Mn |
| Jun 29, 2025 | 848.58 Mn |
| Mar 30, 2025 | 751.93 Mn |
| Dec 29, 2024 | 739.10 Mn |
| Sep 29, 2024 | 738.49 Mn |
| Jun 30, 2024 | 713.54 Mn |
| Mar 31, 2024 | 743.42 Mn |
| Dec 24, 2023 | 700.24 Mn |
| Sep 24, 2023 | 631.67 Mn |
| Jun 25, 2023 | 603.52 Mn |
| Mar 26, 2023 | 663.36 Mn |
| Dec 25, 2022 | 696.19 Mn |
| Sep 25, 2022 | 639.00 Mn |
| Jun 26, 2022 | 621.16 Mn |
| Mar 27, 2022 | 624.53 Mn |
| Dec 26, 2021 | 639.78 Mn |
| Sep 26, 2021 | 604.52 Mn |
API Access
Lam Research 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=LRCX&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "LRCX", "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=LRCX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();