KAYTUS Enhances KSManage with Full-Stack O&M Visibility for AI Data Centers

KSManage is designed for next-gen AI data center, with four-level visibility across components, servers and cabinets, clusters, and AI jobs, and ensures the AI data centers’ high availability

As AI data centers scale to support increasingly complex AI workloads, traditional IT monitoring can no longer provide the visibility required for reliable operations. KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, has significantly upgraded KSManage, introducing full-stack, four-level visibility across components, servers and cabinets, clusters, and AI jobs, to address the challenges of complex troubleshooting, higher component failure rates, intricate application dependencies and delayed responses to operations and maintenance (O&M) incidents generated by demanding AI data center operations. The enhanced platform enables precise fault localization, faster incident response, and proactive operations. With KSManage, KAYTUS helps customers maximize availability, improve operational efficiency, and ensure the stability of mission-critical AI data centers powering next-generation computing.

Four Key Challenges Constrain the Operational Efficiency of AI Data Centers

The rapid evolution of large language models (LLMs) is accelerating the development of AI data centers, driving widespread adoption of heterogeneous CPU, GPU, and DPU architectures and increasing the need for cross-regional collaboration. These trends are significantly raising the complexity of operations and maintenance (O&M), where even a single outage can result in losses exceeding USD 1 million, underscoring the growing importance of availability and resilience in AI data center operations.

1. Infrastructure Complexity Hinders Troubleshooting.

AI heterogeneous data centers integrate a wide range of computing, networking, storage, and supporting systems. Traditional monitoring approaches treat devices as isolated entities and lack end-to-end visibility across the full system, making fault tracking and correlation difficult. As a result, these methods fall short of the stringent operational requirements of AI data centers, which demand rapid detection, rapid analysis, and rapid recovery. The inability to quickly identify root causes directly impacts recovery time and undermines overall system availability.

2. Rising Core Component Failure Rates and Limited Predictive Warning.

Core components such as GPUs and storage devices form the foundation of AI data center performance and operational stability. The rapid adoption of high–power-density hardware has significantly accelerated component wear, driving higher failure rates. Industry data indicate that GPU power consumption has increased more than fivefold over the past decade, while cabinet power density has risen to 20–50 kW, and gradually approaching 200 kW. Under such sustained high-load conditions, the risk of component failure increases sharply. However, traditional monitoring systems lack real-time health tracking and predictive trend analysis, limiting the ability to detect early warning signs and proactively prevent failures.

3. Complex AI Application Scenarios Lack End-to-End Business Correlation for Monitoring.

AI data centers support a wide range of application scenarios, including AI-generated content (AIGC), autonomous driving, and scientific computing. These workloads impose highly diverse requirements on compute, network, and storage resources, making it difficult to correlate underlying hardware issues, such as GPU memory leaks or InfiniBand packet loss, with specific AI jobs. Industry statistics show that approximately 8% of unplanned LLM training interruptions are caused by optical module or fiber failures. Even millisecond-level packet loss can disrupt training, trigger job restarts, and force progress rollbacks, resulting in significant waste of computing resources. Traditional monitoring approaches lack full-link visibility across hardware, workloads, and business processes, limiting their ability to pinpoint and resolve such issues efficiently.

4. Complicated Maintenance Processes Lead to Delayed O&M Responses.

The growing need for cross-regional collaboration has significantly increased the complexity of AI data center operations and maintenance. Critical tasks such as resource scheduling and network link planning still rely heavily on manual processes, which are time-consuming and prone to error. At the same time, limited operational staffing further slows response times, forcing organizations into a largely reactive approach to fault management. The lack of automated response mechanisms results in extended mean time to repair (MTTR), negatively impacting overall service availability and operational efficiency.

KSManage Address the Four Key Challenges by a Full-stack Four-level Intelligent Visibility

To address the operational and maintenance (O&M) challenges of AI data centers, KSManage introduces a newly established four-layer intelligent monitoring framework, spanning from components to systems. Leveraging global, end-to-end visibility, the solution enables automated fault detection, early warning, and intelligent remediation—significantly enhancing O&M efficiency and ensuring the high availability of AI data centers.

1. Full Correlated Visibility with Real-Time Troubleshooting and 3D Visualization

To address the complexity of troubleshooting in large-scale AI data centers driven by heterogeneous infrastructure and densely interwoven relations, KAYTUS KSManage delivers full correlated visibility with unified visual intelligence. The platform continuously collects real-time core metrics, including GPU and CPU utilization, video memory usage, power consumption, network bandwidth, and storage health, while concurrently aggregating operational events and network logs. Leveraging automated topology discovery, KSManage tracks end-to-end cross-node workloads, building an integrated “measurement–log–trace” data foundation. By correlating device health and down to port-level telemetry throughout the entire job lifecycle, KSManage dynamically visualizes resource allocation through real-time 3D modeling. This end-to-end approach overcomes the limitations of traditional siloed monitoring, enabling precise full correlation analysis and transforming root-cause diagnosis from time-consuming investigation into rapid, accurate fault localization, improving troubleshooting efficiency by up to 90%.

2. Predictive Hardware Trend Analysis with Early Warning for Core Component Reliability.

To address the lack of proactive early warning, rising failure rates, and accelerated component wear driven by the widespread adoption of high-power-density devices, KAYTUS KSManage establishes an intelligent hardware health management and early warning system. Leveraging comprehensive hardware telemetry, KSManage applies advanced algorithms to deeply analyze performance trends of critical components, including GPUs and storage devices. Early indicators of abnormal wear are accurately identified, enabling hardware failure risks to be predicted up to seven days in advance. In parallel, KSManage continuously monitors key operational parameters such as load and temperature, proactively mitigating potential failures under sustained high-load conditions and reducing component failure rates at the source.

3. End-to-End Application Dependencies Corelated with Network Monitoring and Workflows.

To address the challenges posed by diverse AI application scenarios, complex business workflows, and the difficulty of correlating hardware anomalies with AI training tasks, KAYTUS KSManage delivers full correlated visibility across hardware, platforms, and workloads. The solution precisely monitors critical network metrics, including bandwidth, latency, and packet loss, while reserving a 20% bandwidth margin to ensure stable data transmission, maintaining millisecond-level internal latency and packet loss below 0.01%. This enables accurate mapping of hardware anomalies to specific training jobs. By tracing the complete path from network anomalies through workloads to business impact, KSManage rapidly pinpoints root causes of LLM training interruptions, such as optical module or fiber faults, preventing training rollbacks, eliminating wasted compute resources, and delivering end-to-end visibility beyond the capabilities of traditional monitoring tools.

4. Four-level automated O&M with Precise Troubleshooting and Rapid Response

To address excessive reliance on manual operations, shortages of specialized O&M personnel, and delayed incident response, KAYTUS KSManage delivers a resilient, intelligent O&M system built on a four-layer visibility framework spanning components, servers and cabinets, clusters, and AI workloads. This unified architecture enables end-to-end automated operations and precise fault diagnosis across the entire AI data center. Automated backup success rates reach nearly 99.8%, while the combined application of knowledge graphs and time-series anomaly detection algorithms enables up to 90% of root causes to be automatically identified within five minutes. As a result, O&M efficiency is increased by up to four times, significantly reducing mean time to repair (MTTR) and minimizing dependence on manual intervention and human error. In parallel, KSManage establishes a resilient response mechanism featuring early warning, tiered protection, and automated isolation and remediation. Storage capacity risks can be predicted up to three days in advance, reducing overall O&M costs and delivering up to a 40% reduction in total cost of ownership (TCO).

Experience KSManage

KSManage is now offered for trial that can be launched in just a few clicks, allowing users to quickly and fully explore the product’s capabilities. To start your trial, please visit: https://ksmanage.kaytus.com (username: admin/password: Manage1!)

For any questions or additional information, please contact us at ksmanage@kaytus.com

Our team will respond promptly!

About KAYTUS

KAYTUS is a leading provider of end-to-end AI and liquid cooling solutions, delivering a diverse range of innovative, open, and eco-friendly products for cloud, AI, edge computing, and other emerging applications. With a customer-centric approach, KAYTUS is agile and responsive to user needs through its adaptable business model. Discover more at KAYTUS.com and follow us on LinkedIn and X

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