Siemens – Siemens and nVent to release joint reference architecture purpose-built for NVIDIA AI data centers

EMR Analysis

More information on Siemens AG: See full profile on EMR Executive Services

More information on Dr. Roland Busch (President and Chief Executive Officer, Siemens AG): See full profile on EMR Executive Services

 

More information on Siemens Smart Infrastructure (SI) by Siemens AG: https://new.siemens.com/global/en/company/about/businesses/smart-infrastructure.html + Siemens Smart Infrastructure (SI) is shaping the market for intelligent, adaptive infrastructure for today and the future. It addresses the pressing challenges of urbanization and climate change by connecting energy systems, buildings, and industries. SI provides customers with a comprehensive end-to-end portfolio from a single source – with products, systems, solutions, and services from the point of power generation all the way to consumption. With an increasingly digitalized ecosystem, it helps customers thrive and communities progress while contributing toward protecting the planet. To protect this journey, we foster holistic cybersecurity to ensure secure and reliable operations. Siemens Smart Infrastructure has its global headquarters in Zug, Switzerland. As of September 30, 2025, the business had around 79,400 employees worldwide. 

More information on Matthias Rebellius (Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG + Member of the Supervisory Board, Siemens Energy AG): See the full profile on EMR Executive Services

 

More information on Ciaran Flanagan (Global Head of Data Center Solutions & Services, Siemens Smart Infrastructure (SI), Siemens AG): See the full profile on EMR Executive Services

 

 

 

More information on nVent: See full profile on EMR Executive Services

More information on Beth Wozniak (Chair & Chief Executive Officer, nVent): See full profile on EMR Executive Services

 

More information on Systems Protection (formerly Enclosures) by nVent: See full profile on EMR Executive Services

More information on Sara Zawoyski (President, Systems Protection (formerly Enclosures), nVent): See full profile on EMR Executive Services

 

 

 

More information on NVIDIA: https://www.nvidia.com/en-us/ + NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world’s largest industries and profoundly impacting society.

Founded in 1993, NVIDIA is the world leader in accelerated computing. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, revolutionized accelerated computing, ignited the era of modern AI, and is fueling industrial digitalization across markets. NVIDIA is now a full-stack computing infrastructure company with data-center-scale offerings that are reshaping industry.

More information on Jensen Huang (Chief Executive Officer, NVIDIA): https://nvidianews.nvidia.com/bios + https://www.linkedin.com/in/jenhsunhuang/ 

 

 

 

More information on Uptime Institute: https://uptimeinstitute.com/ + Uptime Institute is an unbiased advisory organization focused on improving the performance, efficiency, and reliability of business critical infrastructure through innovation, collaboration, and independent performance certifications.

Uptime Institute serves all stakeholders responsible for IT service availability through industry leading standards, education, network, consulting, and award programs delivered to enterprise organizations and third-party operators, manufacturers, and providers. Uptime Institute is recognized globally for the creation and administration of the Tier Standards & Certifications for Data Center Design, Construction, and Operational Sustainability along with its Management & Operations reviews, Digital Infrastructure Resiliency Assessment, FORCSS® methodology, accredited infrastructure training programs and the Efficient IT Stamp of Approval.

Through our globally respected Tier Standards and other program offerings, we’ve helped enterprise and vendor organizations around the globe build and maintain business-critical infrastructure to optimize performance, reliability, and efficiency. We have awarded over 2500 Tier Certifications in more than 110 countries and trained over five-thousand professionals with our Accredited Tier educational courses.

More information on Martin McCarthy (Chief Executive Officer, Uptime Institute): https://uptimeinstitute.com/about-ui/our-team + https://www.linkedin.com/in/martin-mccarthy-550b9519/

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.

 

• Hybrid Computing: 
  • A hybrid cloud integrates private, on-premises infrastructure with public cloud services, offering flexibility to distribute workloads between these environments. Hybrid models often incorporate edge computing, allowing organizations to run critical workloads locally at the edge while using the cloud for other tasks, thereby optimizing performance, cost, and data management for various business needs.
• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.

 

• Data Centers:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
• White Space and Grey Space in Data Centers:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.

 

• Edge & Cloud Services: 
  • Edge services perform data processing on local devices and servers near the data source, reducing latency for time-sensitive operations, while cloud services centralize large computations and storage in remote datacenters, offering massive scalability and flexibility for general workloads.
  • Most organizations use both, creating an “edge-to-cloud” architecture where edge devices handle immediate tasks, and the cloud manages large-scale data processing and complex applications, providing a seamless and efficient experience.

 

• Tier I, Tier II, Tier III and Tier IV Data Centers: 
  • Classification level used mainly in data centers to describe their reliability, redundancy, and uptime capability.
    The system comes from the Uptime Institute, which defines four tiers (I–IV).

 

 

 

• Blueprint:
  • A blueprint is a guide for making something — it’s a design or pattern that can be followed. Want to build the best tree house ever? Draw up a blueprint and follow the design carefully. The literal meaning of a blueprint is a paper — which is blue — with plans for a building printed on it.
  • After the paper was washed and dried to keep those lines from exposing, the result was a negative image of white (or whatever color the blueprint paper originally was) against a dark blue background. The resulting image was therefore appropriately named “blueprint.”.
  • By definition, a blueprint is a drawing up of a plan or model. The blueprint perspective allows you to see all the pieces needed to assemble your business before you begin.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.

 

 

• Tokens-Per-Watt:
  • Tokens-per-watt is a data-center efficiency metric for AI workloads. It measures how many AI tokens (text processed or generated by an LLM) a data center can produce per watt of power consumed. The higher the number, the more energy-efficient the AI compute is.

 

 

• Fundamental Units of Electricity:
  • Ampere – Amp (A):
    • Amperes measure the flow of electrical current (charge) through a circuit. Ampere (A) is the unit of measure for the rate of electron flow, or current, in an electrical conductor.
      • One ampere is defined as one coulomb of electric charge moving past a point in one second. The ampere is named after the French physicist André-Marie Ampère, who made significant contributions to the study of electromagnetism.
      • Milliampere (mA) is a unit of electric current equal to one-thousandth of an ampere (1mA=10−3A). The prefix “milli” signifies 10−3 in the metric system. This unit is commonly used to measure small currents in electronic circuits and consumer devices.
    • Volts measure the force or potential difference that drives the flow of electrons through a circuit.
      • Kilovolt (kV) is a unit of potential difference equal to 1,000 volts.
    • Watts measure the rate of energy consumption or generation, also known as power.
  • Power vs. Energy: how electricity is measured and billed.
    • Power (measured in kW, MW, GW, TW): Rate at which energy is used or generated at a given moment.
    • Energy (measured in kWh, MWh, GWh, TWh): Total amount of power consumed or generated over a period of time (i.e., Power x Time).
  • Real Power Units: actual power that performs work.
    • Kilowatt (KW):
      • A kilowatt is simply a measure of how much power an electric appliance consumes—it’s 1,000 watts to be exact. You can quickly convert watts (W) to kilowatts (kW) by dividing your wattage by 1,000: 1,000W 1,000 = 1 kW.
    • Megawatt (MW):
      • One megawatt equals one million watts or 1,000 kilowatts, roughly enough electricity for the instantaneous demand of 750 homes at once.
    • Gigawatt (GW):
      • A gigawatt (GW) is a unit of power, and it is equal to one billion watts.
      • According to the Department of Energy, generating one GW of power takes over three million solar panels or 310 utility-scale wind turbines
    • Terawatt (TW):
      • One terawatt is equal to one trillion watts (1,000,000,000,000 watts). The main use of terawatts is found in the electric power industry, particularly for measuring very large-scale power generation or consumption.
      • According to the United States Energy Information Administration, America is one of the largest electricity consumers in the world, using about 4,146.2 terawatt-hours (TWh) of energy per year.
  • Apparent Power Units: measures the total power in a circuit, including power that does not perform useful work.
    • Kilovolt-Amperes (kVA):
      • Kilovolt-Amperes (kVA) stands for Kilo-volt-amperes, a term used for the rating of an electrical circuit. A kVA is a unit of apparent power, which is the product of the circuit’s maximum voltage and current rating.
      • The difference between real power (kW) and apparent power (kVA) is crucial. Real power (kW) is the actual power that performs work, while apparent power (kVA) is the total power delivered to a circuit, including the real power and the reactive power (kVAR) that doesn’t do useful work. The relationship between them is defined by the power factor. Since the power factor is typically less than 1, the kVA value will always be higher than the kW value.
    • Megavolt-Amperes (MVA):
      • Megavolt-Amperes (MVA) is a unit used to measure the apparent power in a circuit, primarily for very large electrical systems like power plants and substations. It’s a product of the voltage and current in a circuit.
      • 1 MVA is equivalent to 1,000 kVA, or 1,000,000 volt-amperes.
  • Specialized Power Units: used specifically for renewable energy, especially solar.
    • KiloWatt ‘peak’ (KWp):
      • kWp stands for kilowatt ‘peak’ power output of a system. It is most commonly applied to solar arrays. For example, a solar panel with a peak power of 3kWp which is working at its maximum capacity for one hour will produce 3kWh. kWp (kilowatt peak) is the total kw rating of the system, the theoretical ‘peak’ output of the system. e.g. If the system has 4 x 270 watt panels, then it is 4 x 0.27kWp = 1.08kWp.
      • The Wp of each panel will allow you to calculate the surface area needed to reach it. 1 kWp corresponds theoretically to 1,000 kWh per year.

 

 

• Extra Low-Voltage (ELV):
  • Extra-Low Voltage (ELV) is defined as a voltage of 50V or less (AC RMS), or 120V or less (ripple-free DC).
• Low-Voltage (LV):
  • The International Electrotechnical Commission (IEC) defines Low Voltage (LV) for supply systems as voltage in the range 50–1000 V AC or 120–1500 V DC.
• Medium-Voltage (MV):
  • Medium Voltage (MV) is a voltage class that typically falls between low voltage and high voltage, with a common range being from 1 kV to 35 kV. In some contexts, this range can extend higher, up to 69 kV.
• High-Voltage (HV):
  • The International Electrotechnical Commission define high voltage as above 1000 V for alternating current, and at least 1500 V for direct current.
• Super High-Voltage or Extra High-Voltage (EHV): 
  • Super High-Voltage or Extra High-Voltage (EHV) is the voltage class used for long-distance bulk power transmission. The range for EHV systems is typically from 230 kV to 800 kV.
• Ultra High-Voltage (UHV): 
  • Ultra High-Voltage (UHV) is the highest voltage class used in electrical transmission, defined as a voltage of 1000 kV or greater.

 

 

• IoT (Internet of Things): 
  • The Internet of Things (IoT) refers to a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention.
  • It describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet.
  • The Most Popular IoT Devices are:
    • Smart watches.
    • Smart thermostats.
    • Voice-activated smart speakers and assistants.
    • Smart locks and security systems.
    • Fitness trackers and connected health monitors.
    • Smart lighting appliances.

• IIoT (Industrial IoT):
  • Industrial IoT (IIoT) involves collecting and analyzing sensor-generated data to support equipment monitoring and maintenance, production process analytics and control, and more. It applies IoT technologies specifically to industrial and manufacturing environments to improve efficiency, productivity, and safety.

• AIoT (Artificial Intelligence of Things):
  • Relatively new term and has recently become a hot topic which combines two of the hottest acronyms, AI (Artificial Intelligence) and IoT (Internet of Things).
  • AIoT is transformational and reciprocally beneficial for both types of technology, as AI adds value to IoT through machine learning capabilities and improved decision-making processes, while IoT adds value to AI through connectivity, signaling, and data exchange.
  • Aim: achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.
• xIoT (xTended Internet of Things):
  • xIoT refers to the “eXtended” Internet of Things. This category encompasses a broad range of connected devices, including:
    • Enterprise IoT devices (cameras, printers, and door controllers).
    • Operational Technology (OT) devices (like PLCs, HMIs, and robotics).
    • Network devices (like switches, Wi-Fi routers, and network-attached storage).

 

 

• Hardware vs. Software vs. Firmware: 
  • Hardware is physical: It’s “real,” sometimes breaks, and eventually wears out.
    • Since hardware is part of the “real” world, it all eventually wears out. Being a physical thing, it’s also possible to break it, drown it, overheat it, and otherwise expose it to the elements.
    • Here are some examples of hardware:
      • Smartphone
      • Tablet
      • Laptop
      • Desktop computer
      • Printer
      • Flash drive
      • Router
  • Software is virtual: It can be copied, changed, and destroyed.
    • Software is everything about your computer that isn’t hardware.
    • Here are some examples of software:
      • Operating systems like Windows 11 or iOS
      • Web browsers
      • Antivirus tools
      • Adobe Photoshop
      • Mobile apps
  • Firmware is virtual: It’s software specifically designed for a piece of hardware
    • While not as common a term as hardware or software, firmware is everywhere—on your smartphone, your PC’s motherboard, your camera, your headphones, and even your TV remote control.
    • Firmware is just a special kind of software that serves a very narrow purpose for a piece of hardware. While you might install and uninstall software on your computer or smartphone on a regular basis, you might only rarely, if ever, update the firmware on a device, and you’d probably only do so if asked by the manufacturer, probably to fix a problem.

 

 

• OEM vs. MRO vs. Integrated Supply:
  • OEM (Original Equipment Manufacturer): 
    • An Original Equipment Manufacturer (OEM) is a company that produces parts and equipment that may be marketed by another manufacturer, often under that manufacturer’s brand name. An OEM can make complete devices or specific components.
    • The term OEM usually refers to products that are made specifically for an original product, whereas aftermarket refers to equipment made by another company that a consumer may use as a replacement.
  • MRO (Maintenance, Repair and Operations):
    • MRO refers to all the activities and supplies needed to keep a company’s facilities and production processes running smoothly. These are supplies consumed in the production process that do not become part of the final product.
    • Examples of MRO items include maintenance tools, replacement parts for equipment, personal protective equipment, cleaning supplies, and office supplies.
  • Integrated Supply:
    • Integrated Supply is a large-scale business strategy for managing the MRO supply chain in a more efficient, end-to-end process. The goal is to improve response time, reduce costs, and cut inventory by leveraging technology to create a closer working relationship between suppliers and buyers.
    • For example, a supplier’s computer system may be set up to deliver real-time data to a buyer’s system, providing up-to-date information on inventory and order status.

 

 

• Liquid Cooling Components:
  • Direct-to-Chip (DTC) Cooling:
    • A Direct-to-Chip (DTC) cooling system is a liquid-cooling technology used to cool high-performance computer chips—such as CPUs, GPUs, and accelerators—by bringing a liquid coolant directly to the chip surface through a cold plate.
    • It is one of the most efficient cooling methods used in data centers, HPC clusters, and AI server farms.
  • Chillers:  
    • Mechanical systems that remove heat from a building’s liquid coolant, typically water, and transfer it to another location to cool the air and maintain comfort. Unlike traditional systems that might cool air directly, chillers generate chilled water that circulates through air handling units (AHUs) within the space to absorb heat, making them essential for cooling large commercial or industrial buildings.
  • Technology Cooling System (TCS): 
    • Broad, non-standard term that generally refers to any integrated system, including chillers, designed to manage heat in technological applications, like data centers or industrial processes, to prevent overheating
  • Condensors:
    • A condenser is a heat exchanger that cools a gas or vapor, causing it to condense into a liquid, a process that releases latent heat. These devices are critical in many systems, such as air conditioning, refrigeration, and power plants, where they use air or water to absorb heat from the vapor, transforming it back into a liquid state.
  • CDU (Coolant Distribution Unit): 
    • A coolant distribution unit contains a pump that circulates coolant through a network of pipes or channels, distributing it to various components like servers, processors, or other high heat components in large, high power devices that need cooling.
    • Coolant Distribution Units are essential in data centers with high-density applications providing close controlled coolant delivery and precise control of the liquid cooling system. They help manage heat loads, reduce power consumption, and increase efficiency and reliability through redundancy.
  • HDU (Heat Dissipation Unit):
    • Unlike a Coolant Distribution Unit (CDU) that rejects heat from the server rack to the chilled water loop of the building, a Heat Dissipation Unit (HDU™) rejects the server heat from the server rack to the white space where building air conditioning would then remove that heat.
  • RDHX (Rear Door Heat Exchanger) – Rack-level Heat Exchange: 
    • Chilled water cooling doors that fit onto a IT rack. The air is pulled through the rack by the fans or pushed through by the server fans for the passive rear door (no fans).