The partnership integrates NVIDIA's full AI stack — GPU hardware, NeMo, and NIM Microservices — into a managed platform designed to help enterprises move from AI proof of concept to operational deployment at scale.
NTT DATA has announced an enterprise AI factory platform built on NVIDIA's full AI stack, aimed at helping organizations close the gap between successful AI pilots and production systems.
The platform integrates NVIDIA's GPU-accelerated computing, high-performance networking, and AI Enterprise software — including the NeMo framework for building agentic AI systems and NIM Microservices, pre-built GPU-optimized containers with API access. The combined stack is designed to cover the full AI lifecycle, from model training through enterprise application deployment, within what NTT DATA describes as a governed, repeatable framework.
"Enterprises are now seeking robust, scalable platforms that can successfully transition their AI initiatives from pilot projects to full-scale production," said John Fanelli, Vice President of Enterprise Software at NVIDIA, in a statement provided to AI News.
Abhijit Dubey, CEO of NTT DATA, framed the announcement as a response to a persistent enterprise problem. "By integrating NVIDIA technologies into our enterprise AI factories, we're giving clients a powerful and secure environment to adopt agentic AI with measurable returns from the start," he said.
Three early-adopter deployments illustrate the model's range: a cancer-research hospital using NVIDIA HGX infrastructure for radiology analysis and rapid model evaluation; an automotive supplier validating production workloads on bare metal before scaling through the AI factory architecture; and a US technology company using GPU-accelerated simulation to validate a next-generation battery production line before physical deployment.
NTT DATA describes itself as the only global IT services provider active in all three of NVIDIA's partner tracks — Solution Provider, Cloud Partner, and Global System Integrator Partner Network.
The announcement reflects a broader enterprise pressure to demonstrate financial returns on AI spending, with governance and domain-specific performance increasingly serving as the criteria by which AI investments are judged.