Nvidia is speeding up 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 using 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 working to address disruptions in its processes to meet the immense demand for its chips.

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

Nvidia has already sold millions of AI chips. Each chip is tested on a specialized circuit board before sale to ensure it meets design goals such as speed and other functionalities. However, many of the chips used in these test circuit boards have a decade-long history. This poses challenges for testing AI chips that require 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 the venture 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 have been developed that enhance the performance of testing 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 both companies indicated that testing of Nvidia's graphics processing units (GPUs) could be sped up by rates between 30% and 90%.

Menlo Micro's CEO Russ Garcia refrained from disclosing how much work the startup has done with Nvidia, but he noted that other major chip manufacturers have also begun to use these switching chips for test boards. In an interview, Garcia stated, "As a result, if you don’t verify 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."