Latest news with #KAYTUS
Business Wire
26-06-2025
- Business
- Business Wire
KAYTUS Enhances KSManage for Intelligent Management of Liquid-Cooled AI Data Centers
SINGAPORE--(BUSINESS WIRE)--KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, has announced the release of the enhanced KSManage V2.3, its advanced device management platform for AI data centers. The latest version introduces expanded monitoring and control capabilities tailored for GB200 and B200 systems, including integrated liquid cooling detection features. Leveraging intelligent automation, KSManage V2.3 enables AI data centers to operate with greater precision, efficiency, and sustainability, delivering comprehensive refined management across IT infrastructure and maximizing overall performance. As Generative AI technology accelerates, AI data centers have emerged as critical infrastructure for enabling innovations in artificial intelligence and big data. Next-generation devices such as NVIDIA's B200 and GB200 are being rapidly adopted to meet growing AI compute demands. However, their advanced architectures differ substantially from traditional systems, driving the need for more sophisticated management solutions. For instance, the GB200 integrates two B200 Blackwell GPUs with an Arm-based Grace CPU, creating a high-performance configuration that poses new management challenges. From hardware status monitoring to software scheduling, more precise and intelligent control mechanisms are essential to maintain operational efficiency. Moreover, the elevated computing power of these devices leads to higher energy consumption, increasing the risk of performance bottlenecks, or even system outages in the event of failures. As a result, energy efficiency and real-time system monitoring have become mission-critical for ensuring the stability and sustainability of AI data center operations. KSManage Provides Intelligent, Refined Management for AI Data Centers KSManage builds on a wealth of experience in traditional device management and supports more than 5,000 device models. Its comprehensive management framework spans IT, network, security, and other infrastructure components. The platform enables real-time monitoring of critical server components, including CPU, memory, and storage drives. Leveraging intelligent algorithms, KSManage can predict potential faults, issue early warnings, and support preventive maintenance, helping ensure servers operate at peak performance and reducing the risk of unplanned downtime. The upgraded KSManage delivers comprehensive monitoring of key performance indicators for GB200 and B200 devices, including GPU performance, CPU utilization, and memory bandwidth. Through 3D real-time modeling, it dynamically visualizes resource distribution and intelligently adjusts allocation based on workload demands. The platform also features automated network topology management, enabling real-time optimization of NVLink connectivity, and contributing to a 90% boost in operational efficiency. During large model training, KSManage automatically allocates more computing resources to relevant tasks, optimizing the distribution of CPU, GPU, and other components. This ensures higher device utilization, improved computational efficiency, and significantly faster training times. Specific for intelligent fault detection, the upgraded KSManage introduces a three-tier monitoring framework spanning the component, machine, and cluster levels. At the component level, it leverages the PLDM protocol to enable precise monitoring of critical metrics such as GPU memory status. When computational errors are detected in B200 GPUs, KSManage rapidly analyzes error logs to distinguish between hardware faults and software conflicts, achieving over 92% accuracy in fault localization and taking timely corrective actions. At the machine level, KSManage integrates both BMC out-of-band logs and OS in-band logs to support fast and reliable hardware diagnostics. At the cluster level, federated management technology enables cross-domain alarm correlation and analysis, and triggers self-healing engines capable of responding to risks within seconds. The system also synchronizes with a high-precision liquid leak monitoring solution to enhance equipment safety. Collectively, these capabilities significantly reduce Mean Time to Repair (MTTR) and improve Mean Time Between Failures (MTBF), ensuring higher stability and resilience across AI data center operations. Intelligent Management of Green, Liquid-Cooled AI Data Centers As power density in AI data centers continues to increase, cooling has become a critical factor influencing both device performance and operational lifespan. To address this challenge, liquid cooling technology—recognized for its high thermal efficiency—has been widely adopted across next-generation AI infrastructure. The upgraded KSManage introduces a new liquid cooling detection feature that enhances both the efficiency and safety of liquid cooling operations in AI data centers. The system provides real-time monitoring of key parameters such as coolant flow rate, temperature, and pressure, ensuring stable and optimal performance of the liquid cooling infrastructure. By analyzing data from chip power consumption and cooling circuit pressure, KSManage employs a multi-objective optimization algorithm to dynamically adjust flow rates and calculate the optimal coolant distribution under varying workloads. Powered by AI-driven precision control, the platform achieves a 50% improvement in flow utilization and delivers up to 10% additional energy savings in the liquid cooling system. In addition, KSManage enhances operational reliability by providing real-time anomaly detection in the liquid cooling system. When issues such as abnormal flow rates, pressure fluctuations, temperature control failures, or condensation are detected, the system triggers instant alerts and delivers detailed fault diagnostics, enabling maintenance teams to quickly identify and resolve problems. In the event of a critical coolant leak, KSManage coordinates with the Coolant Distribution Unit (CDU) to deliver a millisecond-level response. Upon detection, the system immediately shuts off coolant flow and initiates an automatic power-down of the CDU, ensuring maximum protection of devices and infrastructure. For high-power devices such as the GB200 and B200, KSManage delivers fine-grained energy consumption management at the GPU level. It dynamically adjusts the Thermal Design Power (TDP) thresholds of H100/B200 GPUs, while integrating intelligent temperature regulation technologies—such as variable-frequency fluorine pumps—within the liquid cooling system. These optimizations help reduce Power Usage Effectiveness (PUE) to below 1.3. Additionally, the platform's power-environment interaction module leverages AI algorithms to predict potential cooling system failures. Through synergistic optimization of computing power and energy consumption, KSManage reduces the power usage per cabinet by 20%, effectively lowering device failure rates and improving overall energy efficiency. KSManage has been successfully deployed across a wide range of industries globally, including internet, finance, and telecommunications. With its intelligent, refined, and sustainable management capabilities, it has become an essential tool for overseeing device operations in AI data centers. In one notable case, an AI data center in Central Asia achieved more than a fourfold increase in operational efficiency by leveraging KSManage's intelligent diagnostic features. Device fault handling time was also reduced by 80%. Monitoring and control of the liquid cooling system, and firmware optimization collectively contributed to a 20% reduction in energy consumption. Additionally, the hardware service lifespan was extended by one to two years. KSManage continues to play a critical role in ensuring the efficient, stable, and sustainable operation of AI data center infrastructure. 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 and follow us on LinkedIn and X
Associated Press
12-06-2025
- Business
- Associated Press
KAYTUS Unveils Upgraded MotusAI to Accelerate LLM Deployment
SINGAPORE--(BUSINESS WIRE)--Jun 12, 2025-- KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, today announced the release of the latest version of its MotusAI AI DevOps Platform at ISC High Performance 2025. The upgraded MotusAI platform delivers significant enhancements in large model inference performance and offers broad compatibility with multiple open-source tools covering the full lifecycle of large models. Engineered for unified and dynamic resource scheduling, it dramatically improves resource utilization and operational efficiency in large-scale AI model development and deployment. This latest release of MotusAI is set to further accelerate AI adoption and fuel business innovation across key sectors such as education, finance, energy, automotive, and manufacturing. This press release features multimedia. View the full release here: MotusAI Dashboard As large AI models become increasingly embedded in real-world applications, enterprises are deploying them at scale, to generate tangible value across a wide range of sectors. Yet, many organizations continue to face critical challenges in AI adoption, including prolonged deployment cycles, stringent stability requirements, fragmented open-source tool management, and low compute resource utilization. To address these pain points, KAYTUS has introduced the latest version of its MotusAI AI DevOps Platform, purpose-built to streamline AI deployment, enhance system stability, and optimize AI infrastructure efficiency for large-scale model operations. Enhanced Inference Performance to Ensure Service Quality Deploying AI inference services is a complex undertaking that involves service deployment, management, and continuous health monitoring. These tasks require stringent standards in model and service governance, performance tuning via acceleration frameworks, and long-term service stability, all of which typically demand substantial investments in manpower, time, and technical expertise. The upgraded MotusAI delivers robust large-model deployment capabilities that bring visibility and performance into perfect alignment. By integrating optimized frameworks such as SGLang and vLLM, MotusAI ensures high-performance, distributed inference services that enterprises can deploy quickly and with confidence. Designed to support large-parameter models, MotusAI leverages intelligent resource and network affinity scheduling to accelerate time-to-launch while maximizing hardware utilization. Its built-in monitoring capabilities span the full stack—from hardware and platforms to pods and services—offering automated fault diagnosis and rapid service recovery. MotusAI also supports dynamic scaling of inference workloads based on real-time usage and resource monitoring, delivering enhanced service stability. Comprehensive Tool Support to Accelerate AI Adoption As AI model technologies evolve rapidly, the supporting ecosystem of development tools continues to grow in complexity. Developers require a streamlined, universal platform to efficiently select, deploy, and operate these tools. The upgraded MotusAI provides extensive support for a wide range of leading open-source tools, enabling enterprise users to configure and manage their model development environments on demand. With built-in tools such as LabelStudio, MotusAI accelerates data annotation and synchronization across diverse categories, improving data processing efficiency and expediting model development cycles. MotusAI also offers an integrated toolchain for the entire AI model lifecycle. This includes LabelStudio and OpenRefine for data annotation and governance, LLaMA-Factory for fine-tuning large models, Dify and Confluence for large model application development, and Stable Diffusion for text-to-image generation. Together, these tools empower users to adopt large models quickly and boost development productivity at scale. Hybrid Training-Inference Scheduling on the Same Node to Maximize Resource Efficiency Efficient utilization of computing resources remains a critical priority for AI startups and small to mid-sized enterprises in the early stages of AI adoption. Traditional AI clusters typically allocate compute nodes separately for training and inference tasks, limiting the flexibility and efficiency of resource scheduling across the two types of workloads. The upgraded MotusAI overcomes traditional limitations by enabling hybrid scheduling of training and inference workloads on a single node, allowing for seamless integration and dynamic orchestration of diverse task types. Equipped with advanced GPU scheduling capabilities, MotusAI supports on-demand resource allocation, empowering users to efficiently manage GPU resources based on workload requirements. MotusAI also features multi-dimensional GPU scheduling, including fine-grained partitioning and support for Multi-Instance GPU (MIG), addressing a wide range of use cases across model development, debugging, and inference. MotusAI's enhanced scheduler significantly outperforms community-based versions, delivering a 5× improvement in task throughput and 5× reduction in latency for large-scale POD deployments. It enables rapid startup and environment readiness for hundreds of PODs while supporting dynamic workload scaling and tidal scheduling for both training and inference. These capabilities empower seamless task orchestration across a wide range of real-world AI scenarios. 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 and follow us on LinkedIn and X. View source version on CONTACT: Media Contacts [email protected] KEYWORD: EUROPE SINGAPORE SOUTHEAST ASIA ASIA PACIFIC INDUSTRY KEYWORD: APPS/APPLICATIONS TECHNOLOGY OTHER TECHNOLOGY SOFTWARE NETWORKS INTERNET HARDWARE DATA MANAGEMENT ARTIFICIAL INTELLIGENCE SOURCE: KAYTUS Copyright Business Wire 2025. PUB: 06/12/2025 07:11 AM/DISC: 06/12/2025 07:10 AM
Yahoo
12-06-2025
- Business
- Yahoo
KAYTUS Unveils Upgraded MotusAI to Accelerate LLM Deployment
Streamlined inference performance, tool compatibility, resource scheduling, and system stability to fast-track large AI model deployment. SINGAPORE, June 12, 2025--(BUSINESS WIRE)--KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, today announced the release of the latest version of its MotusAI AI DevOps Platform at ISC High Performance 2025. The upgraded MotusAI platform delivers significant enhancements in large model inference performance and offers broad compatibility with multiple open-source tools covering the full lifecycle of large models. Engineered for unified and dynamic resource scheduling, it dramatically improves resource utilization and operational efficiency in large-scale AI model development and deployment. This latest release of MotusAI is set to further accelerate AI adoption and fuel business innovation across key sectors such as education, finance, energy, automotive, and manufacturing. As large AI models become increasingly embedded in real-world applications, enterprises are deploying them at scale, to generate tangible value across a wide range of sectors. Yet, many organizations continue to face critical challenges in AI adoption, including prolonged deployment cycles, stringent stability requirements, fragmented open-source tool management, and low compute resource utilization. To address these pain points, KAYTUS has introduced the latest version of its MotusAI AI DevOps Platform, purpose-built to streamline AI deployment, enhance system stability, and optimize AI infrastructure efficiency for large-scale model operations. Enhanced Inference Performance to Ensure Service Quality Deploying AI inference services is a complex undertaking that involves service deployment, management, and continuous health monitoring. These tasks require stringent standards in model and service governance, performance tuning via acceleration frameworks, and long-term service stability, all of which typically demand substantial investments in manpower, time, and technical expertise. The upgraded MotusAI delivers robust large-model deployment capabilities that bring visibility and performance into perfect alignment. By integrating optimized frameworks such as SGLang and vLLM, MotusAI ensures high-performance, distributed inference services that enterprises can deploy quickly and with confidence. Designed to support large-parameter models, MotusAI leverages intelligent resource and network affinity scheduling to accelerate time-to-launch while maximizing hardware utilization. Its built-in monitoring capabilities span the full stack—from hardware and platforms to pods and services—offering automated fault diagnosis and rapid service recovery. MotusAI also supports dynamic scaling of inference workloads based on real-time usage and resource monitoring, delivering enhanced service stability. Comprehensive Tool Support to Accelerate AI Adoption As AI model technologies evolve rapidly, the supporting ecosystem of development tools continues to grow in complexity. Developers require a streamlined, universal platform to efficiently select, deploy, and operate these tools. The upgraded MotusAI provides extensive support for a wide range of leading open-source tools, enabling enterprise users to configure and manage their model development environments on demand. With built-in tools such as LabelStudio, MotusAI accelerates data annotation and synchronization across diverse categories, improving data processing efficiency and expediting model development cycles. MotusAI also offers an integrated toolchain for the entire AI model lifecycle. This includes LabelStudio and OpenRefine for data annotation and governance, LLaMA-Factory for fine-tuning large models, Dify and Confluence for large model application development, and Stable Diffusion for text-to-image generation. Together, these tools empower users to adopt large models quickly and boost development productivity at scale. Hybrid Training-Inference Scheduling on the Same Node to Maximize Resource Efficiency Efficient utilization of computing resources remains a critical priority for AI startups and small to mid-sized enterprises in the early stages of AI adoption. Traditional AI clusters typically allocate compute nodes separately for training and inference tasks, limiting the flexibility and efficiency of resource scheduling across the two types of workloads. The upgraded MotusAI overcomes traditional limitations by enabling hybrid scheduling of training and inference workloads on a single node, allowing for seamless integration and dynamic orchestration of diverse task types. Equipped with advanced GPU scheduling capabilities, MotusAI supports on-demand resource allocation, empowering users to efficiently manage GPU resources based on workload requirements. MotusAI also features multi-dimensional GPU scheduling, including fine-grained partitioning and support for Multi-Instance GPU (MIG), addressing a wide range of use cases across model development, debugging, and inference. MotusAI's enhanced scheduler significantly outperforms community-based versions, delivering a 5× improvement in task throughput and 5× reduction in latency for large-scale POD deployments. It enables rapid startup and environment readiness for hundreds of PODs while supporting dynamic workload scaling and tidal scheduling for both training and inference. These capabilities empower seamless task orchestration across a wide range of real-world AI scenarios. 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 and follow us on LinkedIn and X. View source version on Contacts Media Contacts media@ Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Business Wire
12-06-2025
- Business
- Business Wire
KAYTUS Unveils Upgraded MotusAI to Accelerate LLM Deployment
SINGAPORE--(BUSINESS WIRE)-- KAYTUS, a leading provider of end-to-end AI and liquid cooling solutions, today announced the release of the latest version of its MotusAI AI DevOps Platform at ISC High Performance 2025. The upgraded MotusAI platform delivers significant enhancements in large model inference performance and offers broad compatibility with multiple open-source tools covering the full lifecycle of large models. Engineered for unified and dynamic resource scheduling, it dramatically improves resource utilization and operational efficiency in large-scale AI model development and deployment. This latest release of MotusAI is set to further accelerate AI adoption and fuel business innovation across key sectors such as education, finance, energy, automotive, and manufacturing. As large AI models become increasingly embedded in real-world applications, enterprises are deploying them at scale, to generate tangible value across a wide range of sectors. Yet, many organizations continue to face critical challenges in AI adoption, including prolonged deployment cycles, stringent stability requirements, fragmented open-source tool management, and low compute resource utilization. To address these pain points, KAYTUS has introduced the latest version of its MotusAI AI DevOps Platform, purpose-built to streamline AI deployment, enhance system stability, and optimize AI infrastructure efficiency for large-scale model operations. Enhanced Inference Performance to Ensure Service Quality Deploying AI inference services is a complex undertaking that involves service deployment, management, and continuous health monitoring. These tasks require stringent standards in model and service governance, performance tuning via acceleration frameworks, and long-term service stability, all of which typically demand substantial investments in manpower, time, and technical expertise. The upgraded MotusAI delivers robust large-model deployment capabilities that bring visibility and performance into perfect alignment. By integrating optimized frameworks such as SGLang and vLLM, MotusAI ensures high-performance, distributed inference services that enterprises can deploy quickly and with confidence. Designed to support large-parameter models, MotusAI leverages intelligent resource and network affinity scheduling to accelerate time-to-launch while maximizing hardware utilization. Its built-in monitoring capabilities span the full stack—from hardware and platforms to pods and services—offering automated fault diagnosis and rapid service recovery. MotusAI also supports dynamic scaling of inference workloads based on real-time usage and resource monitoring, delivering enhanced service stability. Comprehensive Tool Support to Accelerate AI Adoption As AI model technologies evolve rapidly, the supporting ecosystem of development tools continues to grow in complexity. Developers require a streamlined, universal platform to efficiently select, deploy, and operate these tools. The upgraded MotusAI provides extensive support for a wide range of leading open-source tools, enabling enterprise users to configure and manage their model development environments on demand. With built-in tools such as LabelStudio, MotusAI accelerates data annotation and synchronization across diverse categories, improving data processing efficiency and expediting model development cycles. MotusAI also offers an integrated toolchain for the entire AI model lifecycle. This includes LabelStudio and OpenRefine for data annotation and governance, LLaMA-Factory for fine-tuning large models, Dify and Confluence for large model application development, and Stable Diffusion for text-to-image generation. Together, these tools empower users to adopt large models quickly and boost development productivity at scale. Hybrid Training-Inference Scheduling on the Same Node to Maximize Resource Efficiency Efficient utilization of computing resources remains a critical priority for AI startups and small to mid-sized enterprises in the early stages of AI adoption. Traditional AI clusters typically allocate compute nodes separately for training and inference tasks, limiting the flexibility and efficiency of resource scheduling across the two types of workloads. The upgraded MotusAI overcomes traditional limitations by enabling hybrid scheduling of training and inference workloads on a single node, allowing for seamless integration and dynamic orchestration of diverse task types. Equipped with advanced GPU scheduling capabilities, MotusAI supports on-demand resource allocation, empowering users to efficiently manage GPU resources based on workload requirements. MotusAI also features multi-dimensional GPU scheduling, including fine-grained partitioning and support for Multi-Instance GPU (MIG), addressing a wide range of use cases across model development, debugging, and inference. MotusAI's enhanced scheduler significantly outperforms community-based versions, delivering a 5× improvement in task throughput and 5× reduction in latency for large-scale POD deployments. It enables rapid startup and environment readiness for hundreds of PODs while supporting dynamic workload scaling and tidal scheduling for both training and inference. These capabilities empower seamless task orchestration across a wide range of real-world AI scenarios. 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 and follow us on LinkedIn and X.
Yahoo
14-04-2025
- Business
- Yahoo
KAYTUS Unveils KSManage V2.0, Quadrupling Data Center O&M Efficiency
KSManage V2.0 delivers a one-stop intelligent data center solution, featuring centralized management of 5,000+ IT device models, with one-click fully automated batch configuration SINGAPORE, April 14, 2025--(BUSINESS WIRE)--KAYTUS, a leading provider of end-to-end AI server and liquid cooling solutions, has announced the release of KSManage V2.0, its next-generation data center management platform. The upgraded platform offers broad compatibility with over 5,000 mainstream IT device models, enabling seamless integration across diverse environments. With one-click fully automated batch configuration, KSManage V2.0 boosts management efficiency by up to four times. Leveraging advanced AIOps capabilities, the platform achieves a fault diagnosis accuracy rate of over 98% and reduces energy consumption by 20%. These enhancements significantly optimize operations and maintenance (O&M) for scaled data centers, empowering sustainable and intelligent infrastructure management. With the rapid advancement of cloud computing and AI applications, data centers have scaled at an unprecedented pace—from just over a hundred devices to tens of thousands. This explosive growth presents significant challenges for operations and maintenance (O&M), particularly in managing vast arrays of heterogeneous servers, storage systems, and network equipment. KSManage is purpose-built to address these complexities, delivering intelligent and efficient data center O&M. It tackles key pain points such as the difficulty of managing diverse hardware, low operational efficiency, and inconsistent infrastructure performance. By ensuring reliable, streamlined, and intelligent infrastructure operations, KSManage enables enterprises to focus entirely on driving their core business innovation. Centralized Management, All-in-One Integrated Platform A major challenge in scaled data centers is the management of heterogeneous devices across multiple vendors and models—each with its own management interfaces and protocols. While open-source tools offer basic functionality, their decentralized approach often leads to fragmented resource allocation and increased operational complexity. KSManage V2.0 addresses this with a unified, enterprise-grade platform designed to streamline O&M. It supports a wide range of IT devices from different vendors, offering compatibility with over 5,000 models of servers, storage systems, and network devices. Through standardized interfaces and protocols, KSManage V2.0 enables centralized, out-of-band management of heterogeneous infrastructure at scale—greatly simplifying operations while enhancing efficiency and control. KSManage V2.0 delivers significant upgrades in data center monitoring and management, with enhanced capabilities across health monitoring, performance tracking, inspection management, and network testing tools. These improvements enable granular, component-level health monitoring, a comprehensive view of performance metrics, and customizable inspection workflows—offering a more precise and intelligent monitoring experience. The platform supports both 2D and 3D global visualization, allowing users to monitor key resource metrics such as power consumption, temperature, and capacity in real time. This enhanced visibility empowers operators to proactively track infrastructure status and optimize management efficiency. In addition, KSManage V2.0 can generate customized visual analytics reports within minutes, simplifying data analysis and accelerating data-driven decision-making. Fully Automated Batch Upgrades, Quadrupling Operational Efficiency Server configuration low efficiency remains a major challenge in scaled data centers, where manual firmware upgrades are time-consuming, complex, and prone to human error. To address this, KSManage V2.0 introduces one-click automated batch upgrades, significantly simplifying workflows and boosting O&M efficiency. Complementing this capability, KAYTUS has launched the KSManage Repo, a centralized firmware repository that hosts the latest updates for KAYTUS servers. After registration and entry of device serial numbers (SNs), customers can connect to the official image repository to automatically detect and retrieve the most up-to-date firmware versions in real time. Leveraging both in-band and out-of-band communication channels, KAYTUS servers support full-stack firmware batch upgrades and automated configuration—including BMC, BIOS, CPLD, FRU, NICs, drives, and more—either online or via batch downloads. This automation ensures optimal device performance and delivers up to a 400% increase in maintenance efficiency. AIOps for Enhanced Reliability and Energy Efficiency Scaled data centers often face challenges related to infrastructure stability and excessive energy consumption. Manual monitoring lacks the responsiveness needed for real-time device analysis, while open-source management tools are frequently plagued by security risks, instability, and limited functionality—resulting in delayed fault detection, slow incident resolution, and potential business disruptions. Additionally, insufficient visibility into energy usage contributes to elevated power usage effectiveness (PUE). KSManage V2.0 addresses these issues through built-in AIOps capabilities, integrating intelligent operations throughout the entire lifecycle of fault prediction, alarm reporting, and diagnostics. The platform not only enhances fault response time and system stability but also provides real-time energy consumption tracking, including carbon emissions monitoring. This enables data centers to optimize energy efficiency and supports sustainable, eco-friendly operations aligned with green IT initiatives. Intelligent Prediction and Rapid Diagnosis. KSManage V2.0 takes predictive maintenance and fault diagnosis to the next level with advanced AI-powered capabilities. It supports drive failure prediction up to 15 days in advance, while its memory failure prediction accuracy has improved by 30%. In the event of a fault, AI algorithms are leveraged for both performance and capacity prediction, enabling proactive and informed decision-making. Designed for scaled data centers, KSManage V2.0 can process real-time, billion-level O&M data within seconds, and respond to thousands of alarms in under five seconds. It employs an innovative ETF (Event-Trigger-Free) threshold-free alarm algorithm, achieving an impressive alarm accuracy rate of 95.26%. For diagnostics, KSManage V2.0 actively and passively monitors metric data and collects logs to quickly detect and accurately pinpoint faults—delivering a diagnostic accuracy rate exceeding 98%. These capabilities significantly improve operational resilience and reduce downtime across complex IT environments. Comprehensive Energy Management for Sustainable Operations. KSManage V2.0 delivers robust energy consumption management across a wide range of data center infrastructure—including AI and general-purpose servers, storage systems, network equipment, cooling units, lighting, and power supply devices. Tailored to meet diverse business needs, KSManage V2.0 offers a variety of power consumption control strategies, enabling dynamic workload-based energy adjustments and visual tracking of carbon emissions. By intelligently managing workloads to maintain peak efficiency and avoiding no-load or overload conditions, the platform reduces overall energy consumption by 15% to 20%. In addition, KSManage V2.0 provides predictive energy analytics based on historical data trends, enabling data centers to proactively plan operational and energy strategies. This minimizes the risk of under- or over-supply of energy and supports sustainable, eco-friendly operations aligned with long-term carbon reduction goals. KSManage has been successfully deployed across a wide range of industries, including cloud service providers (CSPs), finance, and telecommunications. In one notable case involving a leading e-commerce platform in Turkey, KSManage effectively addressed critical operational challenges such as inefficient firmware upgrades, error-prone configurations, and slow OS deployments. By automating the management of over 3,000 servers, KSManage reduced firmware upgrade time by 70%, increased configuration accuracy to 99.8%, and enabled the daily deployment of up to 500 servers. These improvements translated into an 80% boost in overall O&M efficiency and a 40% reduction in hardware failure rates. 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 and follow us on LinkedIn and X. View source version on Contacts Media contact media@
