Nvidia is accelerating the AI chip testing process with Menlo Micro.

Nvidia and Menlo Micro announced on Wednesday that they have achieved a significant speed increase in testing artificial intelligence chips by utilizing the startup's technology. This development is part of efforts to reduce severe production bottlenecks. Nvidia, as a central player in the artificial intelligence era, is working to eliminate delays in its processes to meet the immense demand for its chips.

The company is set to release its revenue report before the market closes on Wednesday, and analysts expect sales to reach $56.9 billion with a growth of 56%. However, due to the exceedingly high valuations of AI companies, investors are monitoring for any signs that a potential bubble might burst.

Nvidia has sold millions of AI chips. Each chip is tested on a specialized circuit board before sale to ensure it meets design targets such as speed and other functionalities. However, many of the chips used in these test circuit boards are over a decade old. This situation complicates testing the AI chips, which have the fastest communication speeds and consume large amounts of energy.

To overcome this bottleneck, Nvidia has partnered with Menlo Micro, which separated from GE in 2016. Menlo Micro has secured a total of $227.5 million in funding from Corning and Tony Fadell, one of the creators of the iPhone. As a result of their work, a series of switching chips has been developed that enhance the performance of test boards.

Menlo Micro is speeding up the testing processes of circuit boards using metal switches produced on a microchip scale. In a research paper published on Wednesday, engineers from both companies stated that Nvidia's graphics processing units (GPUs) tests could be accelerated by between 30% and 90%.

Menlo Micro's CEO Russ Garcia refrained from disclosing how much work the startup has done with Nvidia, but indicated that other major chip manufacturers have also started using these switching chips for test boards. Garcia stated in an interview, "As a result, if you do not validate the GPUs before taking the data to a central data center, you will encounter errors and other issues. The only way to quickly validate these chips is this,"