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

Nvidia and Menlo Micro announced on Wednesday that they have achieved significant speed improvements in testing artificial intelligence chips using the startup's technology. This advancement is part of their efforts to reduce severe production bottlenecks. Nvidia, as a central player in the AI era, is working 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 dollars with a growth of 56%. However, given the extremely high valuations of AI companies, investors are watching for any signs that a bubble might burst.

Nvidia has sold millions of AI chips. Each chip is tested on a special circuit board before sale to determine if it meets design goals such as speed and other functionalities. However, many of the chips used on these test circuit boards have a decade-old history. This makes it difficult to test AI chips that have the fastest communication speeds and consume large amounts of energy.

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

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

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