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One of my favorite autumn conferences, the GPU Technology Conference (GTC), tool place a few months ago at the GPU industry leader Nvidia. With artificial intelligence (AI) as one of the use cases, the firm consistently performs a good job of presenting developments in accelerated computing. Nvidia processors are present in everything from autonomous cars to home voice assistants to Cisco Webex endpoints, making them well suited for AI thanks to their high-speed processing capabilities.

The fact that Nvidia offers more than just silicon and assembles entire designed systems that enterprises, technology firms, service providers, and others may use right away is one of its advantages. One such instance is Maxine from Nvidia, which applies AI to communications. Maxine, like the majority of Nvidia’s products, consists of a full set of GPU-accelerated SDKs that use AI to improve audio and video communications. This gives commonplace microphones and cameras cutting-edge capabilities.

With Maxine, for instance, businesses may include background noise reduction into discussions. Due to the widespread use of speech and video in previously unexplored contexts, features like these are now considered standard in communications. The house worker is an obvious use case, but an insurance adjuster may be utilizing a video app on the side of the road or an airline engineer would need to speak to someone on a runway.

One of the most intriguing, cutting-edge characteristics of Maxine is her capacity to re-render the screen, giving the impression that individuals are staring at one other even when their eyes aren’t pointed straight at the camera. When watching a video, we often turn our attention away from the camera and toward the screen. When a person is not really looking elsewhere, this might give the impression that they are. In a different use scenario, someone may read notes on the screen while looking away from the camera. When individuals are physically separated from one another, it may be difficult to maintain digital closeness. Maxine’s eye alignment function can assist…


I assumed the unified communications sector would swarm to Maxine when Nvidia released it and utilize it in place of undertaking internal development. Both RingCentral and Avaya employ the Nvidia solution to allow AI in their respective Spaces products. Media organizations and other businesses that use Maxine for content production have mostly adopted the software.

Increased UCaaS and CCaaS provider penetration is a primary objective, according to what I’ve learned from my interactions with Nvidia. We had a discussion on the future of the industry and how cloud communications would develop, despite the fact that they didn’t provide any kind of plan. According to market trends, I anticipate Nvidia will declare that Maxine has transformed into a cloud-native platform rather than a cumbersome software stack. Although I’m not sure whether it will be at this GTC or its major event in the spring, this seems to be the product’s next evolutionary step.

Currently, Maxine’s AI SDKs for augmented reality, audio effects, and video effects are used by businesses who seek to benefit from it. This isn’t incorrect, although it does involve some laborious development work. Customers could use microservices and easily add functionality if Maxine had a cloud-native design.

Additionally, almost all of the UCaaS/CCaaS providers’ roadmaps are in line with cloud-native design. Until recently, the majority of cloud communication systems were constructed using monolithic software stacks. Since that time, every single one of them—including RingCentral and Avaya—has actively transitioned its back ends to be cloud native. For this group of companies, it would be much simpler to benefit if Nvidia upgraded Maxine to a comparable design.

This change would have a huge benefit for Nvidia. Except for Cisco and Microsoft, none of the cloud communication service providers are big enough to have their own AI teams. In order to implement features more quickly than if they were to create them themselves, their plan is to collaborate on AI. Maxine would be adopted more quickly as a result of the utilization of microservices, which would enable providers to test out new features without having to go through the laborious SDK development process.

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