NVIDIA (BUE:NVDA) Other Long-Term Liabilities : ARS1,217,526 Mil (As of Jan. 2024)

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What is NVIDIA Other Long-Term Liabilities?

NVIDIA's other long-term liabilities for the quarter that ended in Jan. 2024 was ARS1,217,526 Mil.

NVIDIA's quarterly other long-term liabilities increased from Jul. 2023 (ARS395,831 Mil) to Oct. 2023 (ARS484,750 Mil) and increased from Oct. 2023 (ARS484,750 Mil) to Jan. 2024 (ARS1,217,526 Mil).

NVIDIA's annual other long-term liabilities increased from Jan. 2022 (ARS113,564 Mil) to Jan. 2023 (ARS256,077 Mil) and increased from Jan. 2023 (ARS256,077 Mil) to Jan. 2024 (ARS1,217,526 Mil).

NVIDIA Other Long-Term Liabilities Historical Data

The historical data trend for NVIDIA's Other Long-Term Liabilities can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

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NVIDIA Other Long-Term Liabilities Chart

NVIDIA Annual Data

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Other Long-Term Liabilities

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NVIDIA Quarterly Data
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NVIDIA Other Long-Term Liabilities Calculation

Other Long-Term Liabilities are the other liabilities on the balance sheet that do not need to be repaid within the next 12 months, but still need to be repaid over time. For instance, on Wal-Mart's balance sheet, there are items called Long Term obligations under capital leases, deferred income taxes, and redeemable non-controlling interest. These are all Other Long-Term Liabilities.

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NVIDIA (BUE:NVDA) Business Description

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https://www.nvidia.com

Nvidia is a leading developer of graphics processing units. Traditionally, GPUs were used to enhance the experience on computing platforms, most notably in gaming applications on PCs. GPU use cases have since emerged as important semiconductors used in artificial intelligence. Nvidia not only offers AI GPUs, but also a software platform, Cuda, used for AI model development and training. Nvidia is also expanding its data center networking solutions, helping to tie GPUs together to handle complex workloads.