Kyndryl has launched a suite of AI Private Cloud services aimed at helping enterprises design, deploy, and operate artificial intelligence workloads within secure, dedicated environments. The offering combines consulting services with containerized infrastructure, MLOps/LLMOps capabilities, and support for hybrid deployments across private and public clouds. The company is leveraging partnerships with NVIDIA and others to deliver scalable infrastructure optimized for AI workloads.
The AI Private Cloud services include tools for designing high-ROI AI use cases, building prototypes, assessing production readiness, and managing full-scale deployments. The infrastructure is designed to handle a wide range of industry-specific applications, including fraud detection in financial services, diagnostics in healthcare, virtual assistants in telecom, and predictive maintenance in manufacturing. A recently established AI Private Cloud deployment in Japan, powered by Dell AI Factory and NVIDIA technology, demonstrates Kyndryl’s global deployment capabilities.
Kyndryl supports customers through tailored deployment models that match their AI strategies. Services include data science tools, microservices, and AI-specific infrastructure designed to ensure compliance, data sovereignty, and secure handling of sensitive data. The company also offers integration with NVIDIA AI Enterprise software to support both model training and inference workloads within enterprise-grade environments.
- Kyndryl has launched AI Private Cloud services combining infrastructure, consulting, and MLOps support.
- Customers can build, deploy, and scale AI models across private or hybrid cloud environments.
- Use cases span financial services, healthcare, telecom, and manufacturing.
- Infrastructure supports containerization, data privacy, and compliance with global standards.
- Recent deployments include a Japan-based private AI cloud built with Dell and NVIDIA technologies.
“Customers want a reliable, secure and simpler approach to creating and implementing AI and generative AI workloads on the cloud, while meeting their performance requirements — from LLM training on public cloud to inference on a private AI,” said Nicolas Sekkaki, Global Cloud Practice Leader, Kyndryl.
