SAN FRANCISCO/NEW YORK (Reuters) - OpenAI is pushing ahead on its plan to reduce its reliance on Nvidia ( NVDA ) for its chip supply by developing its first generation of in-house artificial-intelligence silicon.

The ChatGPT maker is finalizing the design for its first in-house chip in the next few months and plans to send it for fabrication at Taiwan Semiconductor Manufacturing Co ( TSM ), sources told Reuters. The process of sending a first design through a chip factory is called "taping out."

OpenAI and TSMC declined to comment.

The update shows that OpenAI is on track to meet its ambitious goal of mass production at TSMC in 2026. A typical tape-out costs tens of millions of dollars and will take roughly six months to produce a finished chip, unless OpenAI pays substantially more for expedited manufacturing. There is no guarantee the silicon will function on the first tape out and a failure would require the company to diagnose the problem and repeat the tape-out step.

Inside OpenAI, the training-focused chip is viewed as a strategic tool to strengthen OpenAI's negotiating leverage with other chip suppliers, the sources said. After the initial chip, OpenAI's engineers plan to develop increasingly advanced processors with broader capabilities with each new iteration. 

If the initial tape out goes smoothly, it would enable the ChatGPT maker to mass-produce its first in-house AI chip and potentially test an alternative to Nvidia's ( NVDA ) chips later this year. OpenAI's plan to send its design to TSMC this year demonstrates the startup has made speedy progress on its first design, a process that can take other chip designers years longer.

Big tech companies such as Microsoft ( MSFT ) and Meta have struggled to produce satisfactory chips despite years of effort. The recent market rout triggered by Chinese AI startup DeepSeek has also raised questions about whether fewer chips will be needed in developing powerful models in the future.

The chip is being designed by OpenAI's in-house team led by Richard Ho,  which had doubled in the past months to 40 people, in collaboration with Broadcom ( AVGO ). Ho joined OpenAI more than a year ago from Alphabet's Google where he helped lead the search giant's custom AI chip program. Reuters first reported OpenAI's plans with Broadcom ( AVGO ) last year.

Ho's team is smaller than the large-scale efforts at tech giants such as Google or Amazon ( AMZN ). A new chip design for an ambitious, large-scale program could cost $500 million for a single version of a chip, according to industry sources with knowledge of chip design budgets. Those costs could double to build the necessary software and peripherals around it.

Generative AI model makers like OpenAI, Google and Meta have demonstrated that ever-larger numbers of chips strung together in data centers make models smarter, and as a result, they have an insatiable demand for the chips. 

Meta has said it will spend $60 billion on AI infrastructure in the next year and Microsoft ( MSFT ) has said it will spend $80 billion in 2025. Currently, Nvidia's ( NVDA ) chips are the most popular and hold a market share of roughly 80%. OpenAI is itself participating in the $500 billion Stargate infrastructure program announced by U.S. President Donald Trump last month.

But rising costs and dependence on a single supplier have led major customers such as Microsoft ( MSFT ), Meta and now OpenAI to explore in-house or external alternatives to Nvidia's ( NVDA ) chips. 

OpenAI's in-house AI chip, while capable of both training and running AI models, will initially be deployed on a limited scale, and primarily for running AI models, the sources said. The chip will have a limited role within the company's infrastructure.

To build out an effort as comprehensive as Google or Amazon's ( AMZN ) AI chip program, OpenAI would have to hire hundreds of engineers. 

TSMC is manufacturing OpenAI's AI chip using its advanced 3-nanometer process technology. The chip features a commonly used systolic array architecture with high-bandwidth memory (HBM) - also used by Nvidia ( NVDA ) for its chips - and extensive networking capabilities, sources said.