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NVIDIA (LIM:NVDA) Short-Term Debt : $0 Mil (As of Jan. 2025)


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What is NVIDIA Short-Term Debt?

NVIDIA's Short-Term Debt for the quarter that ended in Jan. 2025 was $0 Mil.

NVIDIA's annual Short-Term Debt stayed the same from Jan. 2023 ($1,250 Mil) to Jan. 2024 ($1,250 Mil) but then declined from Jan. 2024 ($1,250 Mil) to Jan. 2025 ($0 Mil).


NVIDIA Short-Term Debt Historical Data

The historical data trend for NVIDIA's Short-Term Debt 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 Short-Term Debt Chart

NVIDIA Annual Data
Trend Jan16 Jan17 Jan18 Jan19 Jan20 Jan21 Jan22 Jan23 Jan24 Jan25
Short-Term Debt
Get a 7-Day Free Trial Premium Member Only Premium Member Only 999.00 - 1,250.00 1,250.00 -

NVIDIA Quarterly Data
Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22 Apr22 Jul22 Oct22 Jan23 Apr23 Jul23 Oct23 Jan24 Apr24 Jul24 Oct24 Jan25
Short-Term Debt Get a 7-Day Free Trial Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only 1,250.00 1,250.00 - - -

NVIDIA Short-Term Debt Explanation

Short-Term Debt represents the total amount of Long-Term Debt such as bank loans and commercial paper, which is due within one year.


NVIDIA Short-Term Debt Related Terms

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NVIDIA Business Description

Address
2788 San Tomas Expressway, Santa Clara, CA, USA, 95051
Nvidia Corp is an upfront developer of graphics processing unit and a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry. Traditionally, GPU were used to enhanvce experience,now Nvidia offers AI GPUs, and also a software platform, Cuda, used for AI model development and training. The company is also expanding its data center networking solutions, helping to tie GPUs together to handle complex workloads. such as AI, model training and inference, data analytics, scientific computing, and 3D graphics, with vertical-specific optimizations to address industries ranging from healthcare and telecom to automotive and manufacturing.