#Server for HPC workloads

[infomen]

Boost Enterprise Computing with the HexaData HD-H252-3C0 VER GEN001 Server

The HexaData HD-H252-3C0 VER GEN001 is a powerful 2U high-density server designed to meet the demands of enterprise-level computing. Featuring a 4-node architecture with support for 3rd Gen Intel® Xeon® Scalable processors, it delivers exceptional performance, scalability, and energy efficiency. Ideal for virtualization, data centers, and high-performance computing, this server offers advanced memory, storage, and network capabilities — making it a smart solution for modern IT infrastructure. Learn more: HexaData HD-H252-3C0 VER GEN001.

[govindhtech]

A3 Ultra VMs With NVIDIA H200 GPUs Pre-launch This Month

Strong infrastructure advancements for your future that prioritizes AI

To increase customer performance, usability, and cost-effectiveness, Google Cloud implemented improvements throughout the AI Hypercomputer stack this year. Google Cloud at the App Dev & Infrastructure Summit:

Trillium, Google’s sixth-generation TPU, is currently available for preview.

Next month, A3 Ultra VMs with NVIDIA H200 Tensor Core GPUs will be available for preview.

Google’s new, highly scalable clustering system, Hypercompute Cluster, will be accessible beginning with A3 Ultra VMs.

Based on Axion, Google’s proprietary Arm processors, C4A virtual machines (VMs) are now widely accessible

AI workload-focused additions to Titanium, Google Cloud’s host offload capability, and Jupiter, its data center network.

Google Cloud’s AI/ML-focused block storage service, Hyperdisk ML, is widely accessible.

Trillium A new era of TPU performance

Trillium A new era of TPU performance is being ushered in by TPUs, which power Google’s most sophisticated models like Gemini, well-known Google services like Maps, Photos, and Search, as well as scientific innovations like AlphaFold 2, which was just awarded a Nobel Prize! We are happy to inform that Google Cloud users can now preview Trillium, our sixth-generation TPU.

Taking advantage of NVIDIA Accelerated Computing to broaden perspectives

By fusing the best of Google Cloud’s data center, infrastructure, and software skills with the NVIDIA AI platform which is exemplified by A3 and A3 Mega VMs powered by NVIDIA H100 Tensor Core GPUs it also keeps investing in its partnership and capabilities with NVIDIA.

Google Cloud announced that the new A3 Ultra VMs featuring NVIDIA H200 Tensor Core GPUs will be available on Google Cloud starting next month.

Compared to earlier versions, A3 Ultra VMs offer a notable performance improvement. Their foundation is NVIDIA ConnectX-7 network interface cards (NICs) and servers equipped with new Titanium ML network adapter, which is tailored to provide a safe, high-performance cloud experience for AI workloads. A3 Ultra VMs provide non-blocking 3.2 Tbps of GPU-to-GPU traffic using RDMA over Converged Ethernet (RoCE) when paired with our datacenter-wide 4-way rail-aligned network.

In contrast to A3 Mega, A3 Ultra provides:

With the support of Google’s Jupiter data center network and Google Cloud’s Titanium ML network adapter, double the GPU-to-GPU networking bandwidth

With almost twice the memory capacity and 1.4 times the memory bandwidth, LLM inferencing performance can increase by up to 2 times.

Capacity to expand to tens of thousands of GPUs in a dense cluster with performance optimization for heavy workloads in HPC and AI.

Google Kubernetes Engine (GKE), which offers an open, portable, extensible, and highly scalable platform for large-scale training and AI workloads, will also offer A3 Ultra VMs.

Hypercompute Cluster: Simplify and expand clusters of AI accelerators

It’s not just about individual accelerators or virtual machines, though; when dealing with AI and HPC workloads, you have to deploy, maintain, and optimize a huge number of AI accelerators along with the networking and storage that go along with them. This may be difficult and time-consuming. For this reason, Google Cloud is introducing Hypercompute Cluster, which simplifies the provisioning of workloads and infrastructure as well as the continuous operations of AI supercomputers with tens of thousands of accelerators.

Fundamentally, Hypercompute Cluster integrates the most advanced AI infrastructure technologies from Google Cloud, enabling you to install and operate several accelerators as a single, seamless unit. You can run your most demanding AI and HPC workloads with confidence thanks to Hypercompute Cluster’s exceptional performance and resilience, which includes features like targeted workload placement, dense resource co-location with ultra-low latency networking, and sophisticated maintenance controls to reduce workload disruptions.

For dependable and repeatable deployments, you can use pre-configured and validated templates to build up a Hypercompute Cluster with just one API call. This include containerized software with orchestration (e.g., GKE, Slurm), framework and reference implementations (e.g., JAX, PyTorch, MaxText), and well-known open models like Gemma2 and Llama3. As part of the AI Hypercomputer architecture, each pre-configured template is available and has been verified for effectiveness and performance, allowing you to concentrate on business innovation.

A3 Ultra VMs will be the first Hypercompute Cluster to be made available next month.

An early look at the NVIDIA GB200 NVL72

Google Cloud is also awaiting the developments made possible by NVIDIA GB200 NVL72 GPUs, and we’ll be providing more information about this fascinating improvement soon. Here is a preview of the racks Google constructing in the meantime to deliver the NVIDIA Blackwell platform’s performance advantages to Google Cloud’s cutting-edge, environmentally friendly data centers in the early months of next year.

Redefining CPU efficiency and performance with Google Axion Processors

CPUs are a cost-effective solution for a variety of general-purpose workloads, and they are frequently utilized in combination with AI workloads to produce complicated applications, even if TPUs and GPUs are superior at specialized jobs. Google Axion Processors, its first specially made Arm-based CPUs for the data center, at Google Cloud Next ’24. Customers using Google Cloud may now benefit from C4A virtual machines, the first Axion-based VM series, which offer up to 10% better price-performance compared to the newest Arm-based instances offered by other top cloud providers.

Additionally, compared to comparable current-generation x86-based instances, C4A offers up to 60% more energy efficiency and up to 65% better price performance for general-purpose workloads such as media processing, AI inferencing applications, web and app servers, containerized microservices, open-source databases, in-memory caches, and data analytics engines.

Titanium and Jupiter Network: Making AI possible at the speed of light

Titanium, the offload technology system that supports Google’s infrastructure, has been improved to accommodate workloads related to artificial intelligence. Titanium provides greater compute and memory resources for your applications by lowering the host’s processing overhead through a combination of on-host and off-host offloads. Furthermore, although Titanium’s fundamental features can be applied to AI infrastructure, the accelerator-to-accelerator performance needs of AI workloads are distinct.

Google has released a new Titanium ML network adapter to address these demands, which incorporates and expands upon NVIDIA ConnectX-7 NICs to provide further support for virtualization, traffic encryption, and VPCs. The system offers best-in-class security and infrastructure management along with non-blocking 3.2 Tbps of GPU-to-GPU traffic across RoCE when combined with its data center’s 4-way rail-aligned network.

Google’s Jupiter optical circuit switching network fabric and its updated data center network significantly expand Titanium’s capabilities. With native 400 Gb/s link rates and a total bisection bandwidth of 13.1 Pb/s (a practical bandwidth metric that reflects how one half of the network can connect to the other), Jupiter could handle a video conversation for every person on Earth at the same time. In order to meet the increasing demands of AI computation, this enormous scale is essential.

Hyperdisk ML is widely accessible

For computing resources to continue to be effectively utilized, system-level performance maximized, and economical, high-performance storage is essential. Google launched its AI-powered block storage solution, Hyperdisk ML, in April 2024. Now widely accessible, it adds dedicated storage for AI and HPC workloads to the networking and computing advancements.

Hyperdisk ML efficiently speeds up data load times. It drives up to 11.9x faster model load time for inference workloads and up to 4.3x quicker training time for training workloads.

With 1.2 TB/s of aggregate throughput per volume, you may attach 2500 instances to the same volume. This is more than 100 times more than what big block storage competitors are giving.

Reduced accelerator idle time and increased cost efficiency are the results of shorter data load times.

Multi-zone volumes are now automatically created for your data by GKE. In addition to quicker model loading with Hyperdisk ML, this enables you to run across zones for more computing flexibility (such as lowering Spot preemption).

Developing AI’s future

Google Cloud enables companies and researchers to push the limits of AI innovation with these developments in AI infrastructure. It anticipates that this strong foundation will give rise to revolutionary new AI applications.

Read more on Govindhtech.com

[webyildiz]

Intel Xeon is a series of server and workstation CPUs (central processing units) designed and manufactured by Intel. These processors are specifically built for demanding workloads, such as those commonly used in data centers, enterprise-level computing tasks, and high-performance computing. Xeon processors typically have higher core counts, larger cache sizes, and support for more memory than consumer-grade CPUs, as well as features that enhance reliability and security for mission-critical applications. Certainly! Here's an ultimate guide about Intel Xeon processors: Overview: Intel Xeon processors are designed for server and workstation environments, emphasizing performance, reliability, and scalability. Xeon processors are part of Intel's lineup of high-performance CPUs and are optimized for demanding workloads, such as data centers, cloud computing, virtualization, scientific research, and professional applications. Performance and Architecture: Xeon processors are built on the x86 architecture, which provides compatibility with a wide range of software applications. They feature multiple cores and threads, allowing for parallel processing and improved multitasking capabilities. Xeon processors often have larger cache sizes compared to consumer-grade processors, enabling faster access to frequently used data. They support technologies like Turbo Boost, which dynamically increases clock speeds for improved performance, and Hyper-Threading, which allows each physical core to handle multiple threads simultaneously. Generational Improvements: Intel releases new generations of Xeon processors regularly, introducing enhancements in performance, power efficiency, and feature sets. Each generation may be based on a different microarchitecture, such as Haswell, Broadwell, Skylake, Cascade Lake, Ice Lake, etc. Newer generations often offer higher core counts, improved clock speeds, larger cache sizes, and support for faster memory and storage technologies. Enhanced security features, such as Intel Software Guard Extensions (SGX) and Intel Trusted Execution Technology (TXT), are also introduced in newer Xeon processors. Product Segments: Intel categorizes Xeon processors into various product segments based on performance and capabilities. Entry-level Xeon processors provide basic server functionality and are suitable for small businesses, low-demand workloads, and cost-sensitive environments. Mid-range and high-end Xeon processors offer more cores, higher clock speeds, larger caches, and advanced features like support for multiple sockets, massive memory capacities, and advanced virtualization capabilities. Intel also offers specialized Xeon processors for specific workloads, such as Xeon Phi processors for high-performance computing (HPC) and Xeon Scalable processors for data centers and cloud computing. Memory and Connectivity: Xeon processors support various generations of DDR memory, including DDR3, DDR4, and, in more recent models, DDR5. They typically offer support for large memory capacities, allowing servers to accommodate extensive data sets and run memory-intensive applications efficiently. Xeon processors feature multiple high-speed PCIe lanes for connecting peripherals like storage devices, network cards, and GPUs, facilitating high-performance data transfer. Software Ecosystem and Support: Xeon processors are compatible with a wide range of operating systems, including Windows Server, Linux distributions, and virtualization platforms like VMware and Hyper-V. They are well-supported by software vendors and have extensive compatibility with server-class applications, databases, and enterprise software. Intel provides regular firmware updates, software optimization tools, and developer resources to ensure optimal performance and compatibility with Xeon processors. When choosing an Intel

Xeon processor, consider factors such as workload requirements, core counts, clock speeds, memory support, and specific features needed for your application. It's also important to check Intel's product documentation and consult with hardware experts to select the appropriate Xeon processor model for your server or workstation setup.

[infernovm]

Shell’s immersive cooling liquids the first to receive official certification from Intel

Air-cooling methods for processors and servers handling intense high-performance computing (HPC) and AI workloads have proven to be woefully inadequate, driving up energy use and subsequent costs. Alternative methods have emerged, such as liquid immersion cooling, which uses fluid instead of air to keep electronics from overheating, however, as of yet, there hasn’t been a standard to prove the…

[sharon-ai]

Empowering AI Innovation with SharonAI and the NVIDIA L40 GPU

As artificial intelligence (AI), machine learning, and high-performance computing (HPC) continue to evolve, the demand for powerful, scalable infrastructure becomes increasingly urgent. One company rising to meet this demand is SharonAI—a next-generation computing platform designed to provide seamless access to cutting-edge GPU resources. At the core of this offering is the NVIDIA L40 GPU, a powerful graphics processing unit that is engineered to supercharge AI workloads, simulations, and large-scale data processing.

The Growing Need for AI Infrastructure

In recent years, businesses and research institutions have faced mounting challenges in training complex AI models. These models require not just intelligent algorithms but also vast amounts of computing power. Traditional CPUs often fall short in handling the enormous data sets and mathematical computations that underpin today’s AI systems.

This is where SharonAI enters the scene. By offering high-performance GPU resources, SharonAI makes it possible for startups, research teams, and enterprise-level organizations to access the same level of infrastructure once reserved for tech giants.

Why SharonAI Stands Out

SharonAI distinguishes itself by combining three essential components: performance, scalability, and sustainability. Its infrastructure is designed with flexibility in mind, offering both on-demand and term-based GPU server options. Whether you need a single GPU for a brief task or a multi-GPU setup for a month-long project, SharonAI provides the flexibility to choose.

Another compelling aspect of SharonAI is its commitment to eco-conscious innovation. With data centers powered by green energy and plans for net-zero operations, SharonAI is setting a benchmark for sustainability in the tech sector.

The Game-Changer: NVIDIA L40 GPU

The NVIDIA L40 GPU is the technological foundation that powers SharonAI’s infrastructure. Built on the Ada Lovelace architecture, it’s specifically optimized for AI training, rendering, and scientific simulations.

Key Features of the NVIDIA L40 GPU:

18,176 CUDA Cores for ultra-fast parallel processing

568 Tensor Cores for AI acceleration

142 RT Cores for advanced ray tracing

48 GB GDDR6 ECC memory for handling massive data sets

864 GB/s bandwidth to eliminate data bottlenecks

300W power envelope for energy-efficient performance

This GPU is a powerhouse that bridges the gap between data center performance and real-time AI application needs. It enables faster training cycles, more accurate simulations, and fluid high-resolution rendering.

Flexible Deployment Options

SharonAI offers tailored solutions based on client needs. For dynamic workloads, on-demand servers with 1 to 8 NVIDIA L40 GPUs are available at hourly rates. For long-term, stable operations, monthly term-based GPU servers offer dedicated resources at competitive pricing. These options make SharonAI accessible for a wide range of users—from solo developers to enterprise AI teams.

Real-World Applications

The integration of the NVIDIA L40 GPU into SharonAI’s infrastructure opens new frontiers across multiple industries:

Healthcare: Accelerates drug discovery and medical imaging through deep learning.

Finance: Powers real-time fraud detection and predictive analytics.

Entertainment: Enhances CGI rendering and visual effects with ultra-fast processing.

Academia and Research: Supports complex simulations in physics, climate science, and bioinformatics.

These use cases showcase the versatility and power of combining SharonAI with the NVIDIA L40 GPU.

Looking Ahead

The future of computing lies in the convergence of AI, big data, and scalable infrastructure. SharonAI, backed by the incredible performance of the NVIDIA L40 GPU, is playing a pivotal role in this transformation. By democratizing access to high-end GPUs, SharonAI is empowering developers, scientists, and businesses to push the boundaries of what’s possible in AI.

Whether you're running a deep learning pipeline, developing real-time applications, or conducting breakthrough research, SharonAI provides the tools and horsepower to get the job done.

[skywardtelecom]

Server CPU Model Code Analysis

Decoding Server CPU Model Numbers: A Comprehensive Guide

Server CPU model numbers are not arbitrary strings of letters and digits; they encode critical technical specifications, performance tiers, and use-case optimizations. This article breaks down the naming conventions for server processors, using Intel Xeon as a primary example. While other vendors (e.g., AMD EPYC) may follow different rules, the core principles of hierarchical classification and feature encoding remain similar.

1. Brand and Tier Identification

Brand: The prefix identifies the product family. For instance, Intel Xeon denotes a server/workstation-focused processor line.

Tier: Reflects performance and market positioning:

Pre-2017: Tiers were marked by prefixes like E3 (entry-level), E5 (mid-range), and E7 (high-end).

Post-2017: Intel introduced a metal-based tiering system:

Platinum: Models start with 8 or 9 (e.g., 8480H). Designed for mission-critical workloads, these CPUs support maximum core counts, advanced UPI interconnects, and enterprise-grade features.

Gold: Begins with 5 or 6 (e.g., 6448Y). Targets general-purpose servers and balanced performance.

Silver: Starts with 4 (e.g., 4410T). Optimized for lightweight workloads and edge computing.

2. Generation Identifier

The first digit after the tier indicates the processor generation. Higher numbers represent newer architectures:

1: 1st Gen Scalable Processors (2017, Skylake-SP, 14nm).

2: 2nd Gen Scalable Processors (2019, Cascade Lake, 14nm).

3: 3rd Gen Scalable Processors (2020–2021, Ice Lake/Cooper Lake, 10nm/14nm).

4: 4th Gen Scalable Processors (2023, Sapphire Rapids, Intel 7 process, Golden Cove architecture).

Example: In Platinum 8462V, the “4” signifies a 4th Gen (Sapphire Rapids) CPU.

3. SKU Number

The trailing digits (usually 2–3) differentiate SKUs within the same generation and tier. Higher SKU numbers generally imply better performance (e.g., more cores, larger cache):

Example: Gold 6448Y vs. Gold 6468Y: The latter (SKU 68) has more cores and higher clock speeds than the former (SKU 48).

4. Suffix Letters

Suffixes denote specialized features or optimizations:

C: Single-socket only (no multi-CPU support).

N: Enhanced for networking/NFV (Network Functions Virtualization).

T: Long-lifecycle support (10+ years).

Q: Liquid-cooling compatibility.

P/V: Optimized for cloud workloads (P for IaaS, V for SaaS).

Example: 4410T includes the “T” suffix for extended reliability in industrial applications.

5. Architecture and Interconnect Technologies

On-Die Architecture:

Ring Bus (pre-2017): Limited scalability due to latency spikes as core counts increased.

Mesh Architecture (post-2017): Grid-based core layout improves scalability (e.g., up to 40 cores in Ice Lake).

Interconnects:

UPI (Ultra Path Interconnect): Facilitates communication between multiple CPUs. Platinum-tier CPUs often support 3–4 UPI links (10.4–20.8 GT/s).

PCIe Support: Newer generations integrate updated PCIe standards (e.g., Sapphire Rapids supports PCIe 5.0).

Application-Based Selection Guide

High-Performance Computing (HPC): Prioritize Platinum CPUs (e.g., 8480H) with high core counts and UPI bandwidth.

Cloud Infrastructure: Choose P (IaaS) or V (SaaS) variants (e.g., 6454S).

Edge/Telecom: Opt for N-suffix models (e.g., 6338N) with network acceleration.

Industrial/Embedded Systems: Select T-suffix CPUs (e.g., 4410T) for extended lifecycle support.

Server CPU model numbers act as a shorthand for technical capabilities, enabling IT teams to quickly assess a processor’s performance tier, generation, and specialized features. By understanding these codes, organizations can align hardware choices with workload demands—whether deploying AI clusters, cloud-native apps, or ruggedized edge systems. For precise specifications, always cross-reference vendor resources like Intel’s ARK database or AMD’s technical briefs.

[inginitivehost]

GPU Dedicated Servers for AI, ML & HPC Workloads

Discover the power of GPU dedicated servers for AI, machine learning, and high-performance computing. Achieve faster model training, advanced data processing, and superior performance with scalable, enterprise-grade GPU solutions.

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[infomen]

Next-Gen 2U Server from HexaData – High Performance for Cloud & HPC

The HexaData HD-H261-N80 Ver: Gen001 is a powerful 2U quad-node server designed to meet the demands of modern data centers, AI workloads, and virtualization environments. Powered by up to 8 x Intel® Xeon® Scalable processors, it delivers unmatched density, performance, and flexibility.

This high-efficiency server supports Intel® Optane��� memory, VROC RAID, 10GbE networking, and 100G Infiniband, making it ideal for HPC, cloud computing, and enterprise-grade applications.

With robust remote management via Aspeed® AST2500 BMC and redundant 2200W Platinum PSUs, the HD-H261-N80 ensures reliability and uptime for mission-critical workloads.

Learn more and explore configurations: Hexadata HD-H261-N80-Ver: Gen001|2U High Density Server Page

[govindhtech]

Amazon DCV 2024.0 Supports Ubuntu 24.04 LTS With Security

NICE DCV is a different entity now. Along with improvements and bug fixes, NICE DCV is now known as Amazon DCV with the 2024.0 release.

The DCV protocol that powers Amazon Web Services(AWS) managed services like Amazon AppStream 2.0 and Amazon WorkSpaces is now regularly referred to by its new moniker.

What’s new with version 2024.0?

A number of improvements and updates are included in Amazon DCV 2024.0 for better usability, security, and performance. The most recent Ubuntu 24.04 LTS is now supported by the 2024.0 release, which also offers extended long-term support to ease system maintenance and the most recent security patches. Wayland support is incorporated into the DCV client on Ubuntu 24.04, which improves application isolation and graphical rendering efficiency. Furthermore, DCV 2024.0 now activates the QUIC UDP protocol by default, providing clients with optimal streaming performance. Additionally, when a remote user connects, the update adds the option to wipe the Linux host screen, blocking local access and interaction with the distant session.

What is Amazon DCV?

Customers may securely provide remote desktops and application streaming from any cloud or data center to any device, over a variety of network conditions, with Amazon DCV, a high-performance remote display protocol. Customers can run graphic-intensive programs remotely on EC2 instances and stream their user interface to less complex client PCs, doing away with the requirement for pricey dedicated workstations, thanks to Amazon DCV and Amazon EC2. Customers use Amazon DCV for their remote visualization needs across a wide spectrum of HPC workloads. Moreover, well-known services like Amazon Appstream 2.0, AWS Nimble Studio, and AWS RoboMaker use the Amazon DCV streaming protocol.

Advantages

Elevated Efficiency

You don’t have to pick between responsiveness and visual quality when using Amazon DCV. With no loss of image accuracy, it can respond to your apps almost instantly thanks to the bandwidth-adaptive streaming protocol.

Reduced Costs

Customers may run graphics-intensive apps remotely and avoid spending a lot of money on dedicated workstations or moving big volumes of data from the cloud to client PCs thanks to a very responsive streaming experience. It also allows several sessions to share a single GPU on Linux servers, which further reduces server infrastructure expenses for clients.

Adaptable Implementations

Service providers have access to a reliable and adaptable protocol for streaming apps that supports both on-premises and cloud usage thanks to browser-based access and cross-OS interoperability.

Entire Security

To protect customer data privacy, it sends pixels rather than geometry. To further guarantee the security of client data, it uses TLS protocol to secure end-user inputs as well as pixels.

Features

In addition to native clients for Windows, Linux, and MacOS and an HTML5 client for web browser access, it supports remote environments running both Windows and Linux. Multiple displays, 4K resolution, USB devices, multi-channel audio, smart cards, stylus/touch capabilities, and file redirection are all supported by native clients.

The lifecycle of it session may be easily created and managed programmatically across a fleet of servers with the help of DCV Session Manager. Developers can create personalized Amazon DCV web browser client applications with the help of the Amazon DCV web client SDK.

How to Install DCV on Amazon EC2?

Implement:

Sign up for an AWS account and activate it.

Open the AWS Management Console and log in.

Either download and install the relevant Amazon DCV server on your EC2 instance, or choose the proper Amazon DCV AMI from the Amazon Web Services  Marketplace, then create an AMI using your application stack.

After confirming that traffic on port 8443 is permitted by your security group’s inbound rules, deploy EC2 instances with the Amazon DCV server installed.

Link:

On your device, download and install the relevant Amazon DCV native client.

Use the web client or native Amazon DCV client to connect to your distant computer at https://:8443.

Stream:

Use AmazonDCV to stream your graphics apps across several devices.

Use cases

Visualization of 3D Graphics

HPC workloads are becoming more complicated and consuming enormous volumes of data in a variety of industrial verticals, including Oil & Gas, Life Sciences, and Design & Engineering. The streaming protocol offered by Amazon DCV makes it unnecessary to send output files to client devices and offers a seamless, bandwidth-efficient remote streaming experience for HPC 3D graphics.

Application Access via a Browser

The Web Client for Amazon DCV is compatible with all HTML5 browsers and offers a mobile device-portable streaming experience. By removing the need to manage native clients without sacrificing streaming speed, the Web Client significantly lessens the operational pressure on IT departments. With the Amazon DCV Web Client SDK, you can create your own DCV Web Client.

Personalized Remote Apps

The simplicity with which it offers streaming protocol integration might be advantageous for custom remote applications and managed services. With native clients that support up to 4 monitors at 4K resolution each, Amazon DCV uses end-to-end AES-256 encryption to safeguard both pixels and end-user inputs.

Amazon DCV Pricing

Amazon Entire Cloud:

Using Amazon DCV on AWS does not incur any additional fees. Clients only have to pay for the EC2 resources they really utilize.

On-site and third-party cloud computing

Please get in touch with DCV distributors or resellers in your area here for more information about licensing and pricing for Amazon DCV.

Read more on Govindhtech.com

[rainyducktiger]

Data Center Liquid Cooling Market Regional and Global Industry Insights to 2033

Introduction

The exponential growth of data centers globally, driven by the surge in cloud computing, artificial intelligence (AI), big data, and high-performance computing (HPC), has brought thermal management to the forefront of infrastructure design. Traditional air-based cooling systems are increasingly proving inadequate in terms of efficiency and scalability. This has led to the rapid adoption of liquid cooling solutions, which offer higher thermal performance and energy efficiency. The data center liquid cooling market is poised for significant growth through 2032, fueled by the increasing density of IT equipment and a global push for sustainable and energy-efficient data centers.

Market Overview

The global data center liquid cooling market is expected to witness a compound annual growth rate (CAGR) of over 20% from 2023 to 2032. Valued at approximately USD 2.5 billion in 2022, the market is forecasted to surpass USD 12 billion by 2032, according to industry estimates. North America leads the market, followed closely by Europe and Asia-Pacific.

Key drivers include:

Growing need for high-performance computing in AI and ML workloads.

Increase in data center construction across hyperscale, edge, and colocation segments.

Environmental regulations promoting energy efficiency and sustainability.

Download a Free Sample Report:-https://tinyurl.com/34z8dxuk

Market Segmentation

By Type of Cooling

Direct-to-Chip (D2C) Cooling In D2C systems, liquid coolant flows through pipes in direct contact with the chip or processor. These systems are highly effective in cooling high-density servers and are gaining traction in HPC and AI applications.

Immersion Cooling This method involves submerging entire servers in dielectric coolant fluid. Immersion cooling offers superior thermal management and reduced operational noise. It's increasingly used in crypto mining and AI/ML workloads.

Rear Door Heat Exchangers These solutions replace traditional server cabinet doors with heat exchangers that transfer heat from air to liquid. This hybrid approach is popular among data centers looking to enhance existing air cooling systems.

By Component

Coolants (Dielectric fluids, water, glycol, refrigerants)

Pumps

Heat Exchangers

Plumbing systems

Cooling Distribution Units (CDUs)

By Data Center Type

Hyperscale Data Centers

Enterprise Data Centers

Colocation Data Centers

Edge Data Centers

By Application

High-Performance Computing

Artificial Intelligence & Machine Learning

Cryptocurrency Mining

Cloud Service Providers

Banking, Financial Services, and Insurance (BFSI)

Key Market Trends

1. Rising Power Densities

Modern servers used for AI and HPC workloads often exceed power densities of 30 kW per rack, making traditional air cooling impractical. Liquid cooling efficiently handles heat loads upwards of 100 kW per rack, prompting widespread adoption.

2. Sustainability and ESG Goals

With energy consumption by data centers accounting for nearly 1% of global electricity use, companies are under pressure to reduce their carbon footprint. Liquid cooling systems reduce Power Usage Effectiveness (PUE), water usage, and total energy costs, aligning with environmental goals.

3. Edge Computing Growth

The rise of 5G and IoT technologies necessitates edge data centers, which are often space-constrained and located in harsh environments. Liquid cooling is ideal in such scenarios due to its silent operation and compact form factor.

4. Innovation in Coolant Technologies

Companies are investing in advanced non-conductive and biodegradable dielectric fluids. These innovations enhance performance while reducing environmental impact and regulatory compliance costs.

5. Strategic Partnerships and Investments

Major tech players like Google, Microsoft, and Amazon are investing heavily in liquid cooling R&D. Partnerships between data center operators and liquid cooling vendors are accelerating product development and commercialization.

Competitive Landscape

Key Players

Vertiv Group Corp.

Schneider Electric SE

LiquidStack

Submer

Iceotope Technologies

GRC (Green Revolution Cooling)

Asetek

Midas Green Technologies

These companies are focused on product innovation, strategic acquisitions, and expanding into emerging markets to gain a competitive edge.

Recent Developments

In 2023, Microsoft expanded its partnership with LiquidStack to deploy immersion cooling in Azure data centers.

Google announced plans to test immersion cooling in its data centers to improve energy efficiency.

Intel unveiled its open IP immersion cooling design to promote standardized adoption across the industry.

Regional Insights

North America

Dominates the market due to high demand from hyperscale cloud providers and advanced R&D capabilities. The U.S. government's energy regulations also promote adoption of energy-efficient systems.

Europe

Adoption is fueled by strict carbon emission regulations and sustainability initiatives. Countries like Germany, the UK, and the Netherlands are leading the charge.

Asia-Pacific

The fastest-growing region, driven by increasing digitization, rapid cloud adoption, and government-led smart city initiatives. China and India are key markets due to massive data center expansions.

Challenges and Restraints

High Initial Investment: Liquid cooling systems have higher upfront costs compared to traditional air cooling, which can deter smaller operators.

Maintenance Complexity: Requires specialized maintenance and training.

Market Fragmentation: Lack of standardization in liquid cooling solutions can slow down interoperability and integration.

Future Outlook (2024–2032)

The next decade will see mainstream adoption of liquid cooling, especially among hyperscale data centers and AI-focused operations. Regulatory support, combined with a clear ROI on energy savings, will drive adoption across all regions.

Key predictions:

Over 30% of new data centers will incorporate liquid cooling technologies by 2030.

Hybrid cooling systems combining air and liquid methods will bridge the transition period.

Liquid cooling-as-a-service (LCaaS) will emerge, especially for edge deployments and SMEs.

Conclusion

The data center liquid cooling market is at a pivotal point in its growth trajectory. As workloads become more compute-intensive and sustainability becomes non-negotiable, liquid cooling is emerging not just as an alternative—but as a necessity. Stakeholders across the ecosystem, from operators to manufacturers and service providers, are recognizing the benefits in cost, performance, and environmental impact. The next decade will witness liquid cooling go from niche to norm, fundamentally transforming how data centers are designed and operated.

Read Full Report:-https://www.uniprismmarketresearch.com/verticals/chemicals-materials/data-center-liquid-cooling.html

[infernovm]

Dell data center modernization gear targets AI, HPC workloads

Dell Technology has introduced a raft of new products aimed at advancing customers’ data center modernization projects capable of supporting  a variety of workloads, from virtualization and database management to AI inferencing and high-performance computing (HPC). PowerEdge rack servers provide a reliable and future-ready solution for modern data centers. Dell argues that organizations are…

[sharon-ai]

Revolutionizing AI Development with Affordable GPU Cloud Pricing and Flexible Cloud GPU Rental Options

In today’s data-driven world, the demand for high-performance computing is growing at an unprecedented pace. Whether you’re training deep learning models or running complex simulations, access to powerful GPUs can make all the difference. Fortunately, modern platforms now offer cost-effective GPU cloud pricing and flexible cloud GPU rental services, making cutting-edge computing accessible to everyone, from startups to research institutions.

Why Affordable GPU Cloud Pricing Matters

Efficient GPU cloud pricing ensures that businesses and developers can scale their operations without incurring massive infrastructure costs. The ability to access high-end GPUs on a pay-as-you-go model is especially beneficial for AI workloads that require intensive computation.

Budget-Friendly Rates: Platforms are now offering some of the most competitive pricing models in the industry, with hourly rates significantly lower than traditional hyperscalers.

No Hidden Fees: Transparent pricing with no data transfer charges allows users to control their budget while maximizing performance fully.

Diverse GPU Options: From advanced NVIDIA A100s to AMD's latest offerings, users can choose from various GPUs to meet their unique workload requirements.

Cloud GPU Rental: The Key to Flexibility

Cloud GPU rental empowers users to access the right hardware at the right time. This flexibility is ideal for project-based work, startups testing AI models, or research teams running simulations.

On-Demand Access: Users can rent GPUs exactly when they need them—scaling up or down depending on their workflow.

Scalable Solutions: From single-user tasks to enterprise-level needs, modern platforms accommodate all scales of usage with ease.

Secure and Reliable: Enterprise-grade infrastructure housed in Tier III and IV data centers ensures minimal downtime and maximum performance.

Cost-Effective Performance at Your Fingertips

One of the biggest advantages of cloud GPU rental is the massive cost savings. Modern providers offer rates up to 50% lower than traditional cloud platforms, making them an ideal choice for budget-conscious teams.

All-Inclusive Pricing: What you see is what you pay—no extra charges for data transfer or system maintenance.

Tailored for AI & HPC: These platforms are built from the ground up with AI, deep learning, and HPC needs in mind, ensuring high-speed, low-latency performance.

Custom Discounts: Users with long-term needs or bulk usage requirements can take advantage of volume discounts and custom plans.

Designed for Developers and Innovators

Whether you’re building the next breakthrough AI application or analyzing large-scale scientific data, cloud GPU rental services offer the tools you need without the overhead.

Virtual Server Configuration: Customize your virtual environment to fit your project, improving efficiency and cutting waste.

Integrated Cloud Storage: Reliable and scalable cloud storage ensures your data is always accessible, secure, and easy to manage.

Final Thoughts

The landscape of high-performance computing is changing rapidly, and access to affordable GPU cloud pricing and flexible cloud GPU rental is at the heart of this transformation. Developers, researchers, and enterprises now have the freedom to innovate without being held back by hardware limitations or financial constraints. By choosing a provider that prioritizes performance, transparency, and flexibility, you can stay ahead in a competitive digital world.

[passyouritcertificationexamfirst]

AWS Exam Sample Questions 2025?

To effectively prepare for the AWS Certified Solutions Architect – Associate (SAA-C03) exam in 2025, follow these steps:

Understand the Exam Objectives – Review the official AWS exam guide to understand key topics.

Study with Reliable Resources – Use AWS whitepapers, documentation, and online courses.

Practice with Clearcatnet – Utilize Clearcatnet's latest practice tests to assess your knowledge and improve weak areas.

Hands-on Experience – Gain practical experience by working on AWS services in a real or simulated environment.

Review and Revise – Revisit important concepts, practice time management, and take mock tests before the exam.

By following this structured approach, you can confidently prepare and increase your chances of passing the SAA-C03 exam on your first attempt.

Which service allows you to securely connect an on-premises network to AWS?

A) AWS Direct Connect B) Amazon Route 53 C) Amazon CloudFront D) AWS Snowball

 A company wants to host a web application with high availability. Which solution should they use?

A) Deploy on a single EC2 instance with an Elastic IP B) Use an Auto Scaling group across multiple Availability Zones C) Store website files on Amazon S3 and use CloudFront D) Host the application on an Amazon RDS instance

What AWS service allows you to run containerized applications without managing servers?

A) AWS Lambda B) Amazon ECS with Fargate C) Amazon RDS D) AWS Glue

Which AWS storage service provides automatic replication across multiple Availability Zones?

A) Amazon EBS B) Amazon S3 C) Amazon EC2 instance store D) AWS Snowball

How can you restrict access to an S3 bucket to only a specific VPC?

A) Use an IAM role B) Enable AWS Shield C) Use an S3 bucket policy D) Use a VPC endpoint policy

A company is designing a high-performance computing (HPC) solution using Amazon EC2 instances. The workload requires low-latency, high-bandwidth communication between instances. Which EC2 feature should the company use?

A) Placement Groups with Cluster Strategy B) Auto Scaling Groups C) EC2 Spot Instances D) Elastic Load Balancing

A company needs to store logs from multiple applications running on AWS. The logs must be available for analysis for 30 days and then automatically deleted. Which AWS service should be used?

A) Amazon S3 with a lifecycle policy B) Amazon RDS C) Amazon EFS D) Amazon EC2 instance with attached EBS volume

A company wants to run a web application in a highly available architecture using Amazon EC2 instances. The company requires automatic failover and must distribute incoming traffic across multiple instances. Which AWS service should be used?

A) AWS Auto Scaling and Application Load Balancer B) Amazon S3 and Amazon CloudFront C) AWS Lambda and API Gateway D) Amazon RDS Multi-AZ

A company is migrating a database from an on-premises data center to AWS. The database must remain online with minimal downtime during migration. Which AWS service should be used?

A) AWS Database Migration Service (DMS) B) AWS Snowball C) AWS Backup D) AWS Glue

An application running on Amazon EC2 needs to access an Amazon S3 bucket securely. What is the best practice for granting access?

A) Attach an IAM role with S3 permissions to the EC2 instance B) Store AWS access keys on the EC2 instance C) Use a security group to grant access to the S3 bucket D) Create an IAM user and share credentials with the application

For Getting More Questions and PDF Download Visit 👉 WWW.CLEARCATNET.COM

[lakshmiglobal]

Data Center Cooling Systems – Benefits, Differences, and Comparisons

Data centers generate massive amounts of heat due to high-performance computing equipment running 24/7. Efficient cooling systems are essential to prevent overheating, downtime, and hardware failure. This guide explores the benefits, differences, and comparisons of various data center cooling solutions.

Why Is Data Center Cooling Important?

🔥 Prevents overheating and extends server lifespan. 💡 Ensures optimal performance and reduces downtime. 💰 Lowers energy costs by improving efficiency. 🌱 Supports green IT initiatives by reducing carbon footprint.

Types of Data Center Cooling Systems

1. Air-Based Cooling Systems 🌬️

a) Traditional CRAC (Computer Room Air Conditioning) Units ✔ Uses chilled air circulation to cool the room. ✔ Lower upfront cost, but higher long-term energy use. ✔ Best for smaller data centers with low-density racks.

b) CRAH (Computer Room Air Handler) Units ✔ Works with chilled water cooling systems instead of refrigerants. ✔ More energy-efficient than CRAC units. ✔ Suitable for medium to large-scale data centers.

c) Hot & Cold Aisle Containment ✔ Separates hot & cold air to improve cooling efficiency. ✔ Reduces energy consumption by up to 40%. ✔ Ideal for high-density data centers.

2. Liquid-Based Cooling Systems 💧

a) Chilled Water Cooling ✔ Uses chilled water loops to remove heat. ✔ Highly efficient but requires extensive plumbing. ✔ Best for large-scale data centers.

b) Immersion Cooling ✔ Submerges servers in non-conductive liquid. ✔ Extreme efficiency – reduces cooling energy by up to 95%. ✔ Ideal for high-performance computing (HPC) and AI workloads.

c) Direct-to-Chip Liquid Cooling ✔ Coolant circulates directly through server components. ✔ Prevents hotspots and boosts efficiency. ✔ Great for compact, high-density server racks.

3. Free Cooling (Economization) ❄️

✔ Uses outside air or water sources to cool servers. ✔ Low operating costs but depends on climate conditions. ✔ Ideal for data centers in cooler geographic regions.

Comparison: Air Cooling vs. Liquid Cooling

FeatureAir Cooling (CRAC/CRAH)Liquid Cooling (Chilled Water, Immersion)EfficiencyModerateHigh (up to 95% efficiency)Energy CostHigherLowerSpace RequirementLarge footprintCompactUpfront CostLowerHigherMaintenanceSimpleMore complexBest ForSmall/Medium data centersHigh-density, HPC, AI, Large data centers

Key Benefits of Advanced Cooling Solutions

✅ Reduced energy consumption lowers operational costs. ✅ Enhanced server lifespan due to controlled temperatures. ✅ Improved efficiency for high-performance computing. ✅ Lower environmental impact with green cooling solutions.

Final Thoughts: Choosing the Right Cooling System

The best cooling solution depends on factors like data center size, energy efficiency goals, and budget. Traditional air cooling works for smaller setups, while liquid cooling and free cooling are better for high-density or large-scale operations.

Need Help Choosing the Best Cooling System for Your Data Center?

Let me know your data center size, power requirements, and efficiency goals, and I’ll help find the best solution!