NVIDIA has introduced a comprehensive Enterprise Manageability framework for its DGX Spark and GB10 systems, designed to deliver end-to-end lifecycle control for large-scale AI infrastructure. As enterprise AI deployments expand, operational maturity has become a critical requirement, demanding systems that are provisionable, observable, secure, and manageable without disrupting existing IT workflows. The new framework addresses these needs by providing a complete operational standard from initial provisioning through end-of-life retirement, including full support for fully air-gapped environments. The architecture is built around an agentless, SSH-driven execution model that returns bounded, standardized JSON output. This design allows the framework to integrate seamlessly with enterprise configuration management and monitoring pipelines without replacing existing tools. NVIDIA has partnered with Progress Chef, Perforce Puppet, and Canonical Landscape to ensure immediate compatibility with industry-standard orchestration platforms. The framework is structured across six distinct operational phases: procurement and receiving, initial provisioning, ongoing monitoring, maintenance windows, incident response, and retirement. This segmentation explicitly separates read-only collectors from state-changing controllers, aligning with enterprise least-privilege access and change management policies. Provisioning complexity, particularly in restricted networks, is mitigated by the DGX Spark Custom Installation feature. Leveraging cloud-init, an OEM data partition on installation media, and optional on-premises mirrors, IT teams can deploy and maintain known-good configurations across disconnected fleets without requiring custom infrastructure. For diagnostics, the framework provides spark_diagctl.py for remote health assessment and reset_reason_reporter.py for correlating system events, BMC records, and kernel logs to generate structured root-cause assessments. Both utilities output the standardized JSON envelope, enabling unified integration with existing security and monitoring tools. Fleet maintenance is streamlined through spark_updatectl.py, which exposes system update posture and coordinates staged rollouts within approved change windows. The tool manages tightly coupled layers including the kernel, GPU drivers, firmware, and container runtimes, while capturing pre- and post-update evidence and supporting firmware rollback. Security remains a foundational requirement, with role-based access control enforcing strict privilege separation. Companion integration with Canonical Landscape extends Ubuntu fleet management capabilities to DGX Spark, automating compliance reporting, verified boot validation, and encryption-at-rest audits. NVIDIA has released operational reference guides detailing fleet onboarding, provisioning scripts, and integration patterns for Ansible, Tanium, and Landscape. These resources provide production-ready examples to accelerate enterprise adoption. The framework positions AI infrastructure to meet the rigorous governance, scalability, and security standards required for mission-critical production environments.