Artificial intelligence infrastructure startup C-Gen.AI is launching today with a new platform that helps data center operators automate the deployment and maximize the resource utilization of the expensive hardware that powers today’s most advanced AI models.
Everybody knows that AI models can help to automate business processes that were previously performed by humans. But until now, humans have had no choice but to struggle to build and maintain the expensive infrastructure stacks that sit behind those models.
Now, with C-Gen.AI, data center teams can finally get their own dose of AI automation. Its new GPU Orchestration Platform is designed to help those teams set up their AI infrastructure fast and scale it in the most cost-effective way, without wasting any resources.
The startup says it wants to disrupt how AI infrastructure stacks are built. The most advanced AI models today, such as ChatGPT, run on enormous clusters of graphics processing units, which are provisioned and maintained by human operators around the clock. Traditionally, managing this infrastructure has always been a manual, resource-intensive and slow process. Companies are forced to navigate through a maze of cloud infrastructure providers, and deal with problems such as underutilized compute power and struggle to scale.
C-Gen.AI wants to eliminate all of that friction, and to do so it’s using AI itself. C-Gen.AI platform sits atop of its customer’s existing GPU infrastructure, transforming those resources into an “AI supercomputer,” with features for automating cluster deployment, real-time scaling and GPU reuse.
It says its platform can automate the deployment of new AI clusters in minutes, and then closely monitor them to ensure they’re always running smoothly and at maximum efficiency. By dynamically repurposing idle GPU resources for inference tasks, it ensures that nothing goes to waste.
The startup believes it can assist companies of all sizes. For instance, most AI startups struggle with expensive cloud infrastructure bills and endure slow provisioning when it comes to scale. By providing them with an infrastructure platform that can adapt and scale in seconds, AI companies can expand as they grow, without spending thousands of dollars on redesigning and rebuilding their infrastructure stack.
In the case of data center operators, especially smaller ones trying to compete against giants like Amazon Web Services Inc. and Google Cloud, C-Gen.AI says it can help them to better organize their GPU resources, so that nothing goes to waste. By reassigning idle GPUs to handle inference tasks on the fly, it can help them to maximize the revenue-making potential of their AI hardware.
In addition, C-Gen.AI says it can also cater to large enterprises that need to build their own, scalable and resource-efficient AI stacks to remain compliant with regulations.
C-Gen.AI’s expertise in AI infrastructure comes from its founder and Chief Executive Officer Sami Kama, who knows a thing or two about data centers. He previously helped companies such as Nvidia Corp., AWS and CERN optimize the performance of their own AI stacks.
According to Kama, the AI infrastructure industry is plagued with inefficiencies, with massive GPU investments sitting idle, primarily from poor management and slow provisioning. This is a big reason for the soaring costs of AI hardware, he says.
“The infrastructure layer is where most AI projects quietly break down,” Kama explained. “It’s not just about access to GPUs. It’s about the inability to deploy fast enough, the waste that happens between workloads, and the rigidity that locks teams into environments they can’t afford to scale.”
Kama says the cost savings and efficiencies his company can bring to AI hardware teams are going to be substantial. He cites a forecast from Gartner Inc., which suggests that worldwide spending on generative AI will reach $644 billion by the end of the year, up from $124 billion in 2023. The same report warns that much of this expenditure is due to complexity and mounting technical debt, and urges enterprises to invest in infrastructure that can scale intelligently and adapt rapidly to avoid cost overruns.
“If we want enterprise AI to deliver real results, we must fix the foundation it runs on,” Kama said. “That’s the value proposition C-Gen.AI delivers. It’s AI without pain, without waste, at scale.”
Photo: C-Gen.AI
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