Lam Research (LRCX) Operating Expenses (2009 - 2026)
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Analysis
Lam Research (LRCX) Operating Expenses (2009 - 2026) Analysis & Trends
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | SK hynix | 24,576.54 Bn | 24,560.31 Bn | 43.99 Bn | 3.63 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 10,284.32 Bn | 9,899.31 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Nvidia | 4,083.27 Bn | 3,857.43 Bn | 72.14 Bn | 8.41 Bn |
| 4 | Broadcom | 1,582.22 Bn | 1,508.26 Bn | 20.46 Bn | 4.50 Bn |
| 5 | Advanced Micro Devices | 844.94 Bn | 793.16 Bn | 7.21 Bn | 4.38 Bn |
| 6 | Micron Technology | 820.10 Bn | 725.74 Bn | 47.05 Bn | 2.77 Bn |
| 7 | Asml Holding | 581.15 Bn | 536.84 Bn | 5.90 Bn | - |
| 8 | Intel | 308.67 Bn | 171.38 Bn | 7.10 Bn | 4.57 Bn |
| 9 | Applied Materials | 295.64 Bn | 261.08 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Lam Research | 277.80 Bn | 253.85 Bn | 4.30 Bn | 1.02 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 27, 2026 | 1.02 Bn |
| 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 |
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();