Vapor IO connects with Comcast on AI-as-a-service offering

Comcast is one of the early edge network partners for a new AI- and 5G-as-a-service offering operated by Vapor IO that will eventually become available nationwide.

Vapor IO's new micro-cloud offering, called Zero Gap AI, is underpinned by the Nvidia MGX platform with the Nvidia GH200 Grace Hopper Superchip and Supermicro's AI-optimized servers.

In use cases focused on enterprise and smart city applications, Zero Gap AI aims to deliver private 5G and GPU-based "micro-clouds" to locations such as retail storefronts, factory floors and city intersections, the company said.

Zero Gap AI initially will be available as a pilot offering in two markets – Atlanta and Chicago – and will tie into Comcast's network infrastructure there. That activity builds on integration work that Vapor IO and Comcast announced last year focused on tests of low-latency edge services and applications.

"Our low-latency, high-bandwidth network and connectivity services unlock a world of applications for large and small businesses, residences and mobile customers," Comcast Chief Network Officer Elad Nafshi said in a statement. "We're continuously innovating and collaborating with partners like Vapor IO to identify new ways to leverage our network, and Zero Gap AI is a unique opportunity to expand the limits of what we can do together with edge computing services."

Plans to expand

Zero Gap AI will also expand into dozens of other US markets that have access to Vapor IO's "Kinetic Grid" infrastructure, including Dallas, Las Vegas and Seattle.

With the potential to connect more cable headends to the Vapor IO fabric, Vapor IO believes its cable ambitions will extend well beyond Comcast.

"We think this is going to be a big benefit to the cable operators," Vapor IO CEO and founder Cole Crawford said, noting that the company also has "enjoyed a good relationship with CableLabs for several years."

Vapor IO's launch of Zero Gap AI follows the company's buildout of a footprint of network backbone and individual points of presence across 36 US markets (via Vapor IO's own facilities or those of its colocation partners) to put that capability alongside the radio access network (RAN).

Bringing AI to the edge

The broader idea is to bring the kind of as-a-service capability that companies expect from a cloud company without the complexities of having to figure out how to build a wireless network and build out the elements for AI and machine learning. And the inferencing capabilities of AI can then be deployed at the edge – instead of on-premises – to support enterprises that are operating in multiple markets or wide-scale smart city deployments. Once the AI model is trained and set up, the AI inferencing component is used to make predictions and solve tasks.

"Generative AI costs you money. Inferencing makes you money," Crawford said. "And the inferencing action, I think, is where the industry will make a lot of money."

Vapor IO didn't announce any early, specific deployments of Zero Gap AI, but the company did spell out several potential use cases. A retailer, for instance, could use it for AI-assisted automated checkouts without having to deploy expensive AI gear at each store.

'Computer vision' a driving force

In another example, a city could use Zero Gap AI for "computer vision" services to support a pedestrian safety system across hundreds of intersections without having to deploy AI equipment at every corner. Additionally, construction sites could use computer vision with AI inferencing to determine if everyone working there is wearing a hardhat.

In a more specific example, the City of Las Vegas is working on computer vision inferencing capabilities that would take advantage of thousands of cameras deployed around the city for use in areas such as public safety, law enforcement and traffic management.

"All applications we are seeing demand for today use computer vision in some form or another. Computer vision certainly is the biggest driver," Matt Trifiro, Vapor IO's chief marketing officer, said.

"We think large vision models as a basis for how to do inferencing are going to generate more revenue for the industry than large language models, I think, over the next five years," added Crawford.