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 AI era, is trying to address disruptions in its processes to meet the immense demand for its chips.

The company will release its earnings 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 high valuations of AI companies, investors are watching for any signs of a potential bubble bursting.

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 functions. However, many of the chips used in these tests have a decade-old history. This situation complicates the testing of AI chips, which possess 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 raised a total of $227.5 million in funding from the risk capital fund of Tony Fadell, one of the creators of Corning and the iPhone. As a result of their work, a series of switching chips that enhance the performance of test boards have been developed.

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

Menlo Micro CEO Russ Garcia refrained from disclosing how much work the startup has done with Nvidia, but mentioned that other large chip manufacturers are also starting to use these switching chips for their test boards. In an interview, Garcia stated, "As a result, if you don't validate GPUs before bringing data into a central data center, you will encounter errors and other issues. The only way to quickly validate these chips is this."