#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.

[johndjwan]

800G OSFP - Optical Transceivers -Fibrecross

800G OSFP and QSFP-DD transceiver modules are high-speed optical solutions designed to meet the growing demand for bandwidth in modern networks, particularly in AI data centers, enterprise networks, and service provider environments. These modules support data rates of 800 gigabits per second (Gbps), making them ideal for applications requiring high performance, high density, and low latency, such as cloud computing, high-performance computing (HPC), and large-scale data transmission.

Key Features

OSFP (Octal Small Form-Factor Pluggable):

Features 8 electrical lanes, each capable of 100 Gbps using PAM4 modulation, achieving a total of 800 Gbps.

Larger form factor compared to QSFP-DD, allowing better heat dissipation (up to 15W thermal capacity) and support for future scalability (e.g., 1.6T).

Commonly used in data centers and HPC due to its robust thermal design and higher power handling.

QSFP-DD (Quad Small Form-Factor Pluggable Double Density):

Also uses 8 lanes at 100 Gbps each for 800 Gbps total throughput.

Smaller and more compact than OSFP, with a thermal capacity of 7-12W, making it more energy-efficient.

Backward compatible with earlier QSFP modules (e.g., QSFP28, QSFP56), enabling seamless upgrades in existing infrastructure.

Applications

Both form factors are tailored for:

AI Data Centers: Handle massive data flows for machine learning and AI workloads.

Enterprise Networks: Support high-speed connectivity for business-critical applications.

Service Provider Networks: Enable scalable, high-bandwidth solutions for telecom and cloud services.

Differences

Size and Thermal Management: OSFP’s larger size supports better cooling, ideal for high-power scenarios, while QSFP-DD’s compact design suits high-density deployments.

Compatibility: QSFP-DD offers backward compatibility, reducing upgrade costs, whereas OSFP often requires new hardware.

Use Cases: QSFP-DD is widely adopted in Ethernet-focused environments, while OSFP excels in broader applications, including InfiniBand and HPC.

Availability

Companies like Fibrecross,FS.com, and Cisco offer a range of 800G OSFP and QSFP-DD modules, supporting various transmission distances (e.g., 100m for SR8, 2km for FR4, 10km for LR4) over multimode or single-mode fiber. These modules are hot-swappable, high-performance, and often come with features like low latency and high bandwidth density.

For specific needs—such as short-range (SR) or long-range (LR) transmission—choosing between OSFP and QSFP-DD depends on your infrastructure, power requirements, and future scalability plans. Would you like more details on a particular module type or application?

[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

[cameliaferdon]

Intel Introduces New AI Solutions with Xeon 6 and Gaudi 3

Intel has launched its latest AI solutions featuring the Xeon 6 processors and Gaudi 3 AI accelerators. These advancements promise improved performance and efficiency for AI tasks. With the new Xeon 6, you get better AI and HPC workloads, while Gaudi 3 offers enhanced throughput and cost-effectiveness. Perfect for powering the next generation of AI applications! 🚀

#IntelAI #Innovation #TechNews

Read More Here

[sharon-ai]

In the fast-paced digital world of today, businesses and industries are relying more than ever on efficient and scalable solutions for managing their infrastructure. One of the most promising innovations is the combination of cloud computing infrastructure and artificial intelligence (AI). Together, they are transforming how we handle infrastructure asset management and optimizing industries such as energy. This blog will explain how these technologies work together and the impacts that they are having across a wide array of sectors, including in the USA energy markets 

What is Cloud Computing Infrastructure?

Cloud computing infrastructure refers to the systems that serve as the basis for delivering cloud services. This may include virtual servers, storage systems, networking capabilities, and databases. They are offered to businesses and consumers through the internet. Instead of having to hold expensive physical infrastructure, a company can use cloud infrastructure solutions to scale its operations very efficiently.

Businesses do not have to be concerned about the capital expenses for on-premise infrastructure maintenance and upgrades. With cloud service provision, organizations are enabled with tools for the management of cloud infrastructure on digital resources to watch out for them seamlessly. With cloud computing in the energy industry, companies run their simulations and manage the output without having to buy large, expensive hardware.

Changing the Face of Computing Infrastructure

The Role of AI Technology

Artificial intelligence (AI refers to computer systems designed to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. AI is revolutionizing how infrastructure is managed by enabling automated systems to make decisions based on data and real-time analysis.

In the energy industry, for instance, AI technology can be used in analyzing large volumes of data to optimize operations, predict failures, and recommend improvements. Such is vital in industries like US energy markets, where AI solutions can predict market fluctuations, optimize energy distribution, and increase overall efficiency.

Artificial Intelligence in Cloud Computing

When artificial intelligence in cloud computing is introduced, the possibilities expand exponentially. AI-based cloud solutions allow businesses to benefit from AI capabilities without requiring investment in dedicated hardware or a specialized team. For example, companies can utilize AI cloud computing benefits to analyze large data sets stored in the cloud, forecast energy demands, or predict equipment failures in real time.

AI and Cloud Computing for Asset Management

Among the benefits that come from the integration of AI with cloud computing infrastructure is infrastructure asset management. It is complex managing equipment, machines, or even digital services. AI algorithms help in optimizing this by identifying patterns and predicting when assets will require maintenance or replacement.

[news24-amit]

Data Center Accelerator Market Set to Transform AI Infrastructure Landscape by 2031

The global data center accelerator market is poised for exponential growth, projected to rise from USD 14.4 Bn in 2022 to a staggering USD 89.8 Bn by 2031, advancing at a CAGR of 22.5% during the forecast period from 2023 to 2031. Rapid adoption of Artificial Intelligence (AI), Machine Learning (ML), and High-Performance Computing (HPC) is the primary catalyst driving this expansion.

Market Overview: Data center accelerators are specialized hardware components that improve computing performance by efficiently handling intensive workloads. These include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), which complement CPUs by expediting data processing.

Accelerators enable data centers to process massive datasets more efficiently, reduce reliance on servers, and optimize costs a significant advantage in a data-driven world.

Market Drivers & Trends

Rising Demand for High-performance Computing (HPC): The proliferation of data-intensive applications across industries such as healthcare, autonomous driving, financial modeling, and weather forecasting is fueling demand for robust computing resources.

Boom in AI and ML Technologies: The computational requirements of AI and ML are driving the need for accelerators that can handle parallel operations and manage extensive datasets efficiently.

Cloud Computing Expansion: Major players like AWS, Azure, and Google Cloud are investing in infrastructure that leverages accelerators to deliver faster AI-as-a-service platforms.

Latest Market Trends

GPU Dominance: GPUs continue to dominate the market, especially in AI training and inference workloads, due to their capability to handle parallel computations.

Custom Chip Development: Tech giants are increasingly developing custom chips (e.g., Meta’s MTIA and Google's TPUs) tailored to their specific AI processing needs.

Energy Efficiency Focus: Companies are prioritizing the design of accelerators that deliver high computational power with reduced energy consumption, aligning with green data center initiatives.

Key Players and Industry Leaders

Prominent companies shaping the data center accelerator landscape include:

NVIDIA Corporation – A global leader in GPUs powering AI, gaming, and cloud computing.

Intel Corporation – Investing heavily in FPGA and ASIC-based accelerators.

Advanced Micro Devices (AMD) – Recently expanded its EPYC CPU lineup for data centers.

Meta Inc. – Introduced Meta Training and Inference Accelerator (MTIA) chips for internal AI applications.

Google (Alphabet Inc.) – Continues deploying TPUs across its cloud platforms.

Other notable players include Huawei Technologies, Cisco Systems, Dell Inc., Fujitsu, Enflame Technology, Graphcore, and SambaNova Systems.

Recent Developments

March 2023 – NVIDIA introduced a comprehensive Data Center Platform strategy at GTC 2023 to address diverse computational requirements.

June 2023 – AMD launched new EPYC CPUs designed to complement GPU-powered accelerator frameworks.

2023 – Meta Inc. revealed the MTIA chip to improve performance for internal AI workloads.

2023 – Intel announced a four-year roadmap for data center innovation focused on Infrastructure Processing Units (IPUs).

Gain an understanding of key findings from our Report in this sample - https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=82760

Market Opportunities

Edge Data Center Integration: As computing shifts closer to the edge, opportunities arise for compact and energy-efficient accelerators in edge data centers for real-time analytics and decision-making.

AI in Healthcare and Automotive: As AI adoption grows in precision medicine and autonomous vehicles, demand for accelerators tuned for domain-specific processing will soar.

Emerging Markets: Rising digitization in emerging economies presents substantial opportunities for data center expansion and accelerator deployment.

Future Outlook

With AI, ML, and analytics forming the foundation of next-generation applications, the demand for enhanced computational capabilities will continue to climb. By 2031, the data center accelerator market will likely transform into a foundational element of global IT infrastructure.

Analysts anticipate increasing collaboration between hardware manufacturers and AI software developers to optimize performance across the board. As digital transformation accelerates, companies investing in custom accelerator architectures will gain significant competitive advantages.

Market Segmentation

By Type:

Central Processing Unit (CPU)

Graphics Processing Unit (GPU)

Application-Specific Integrated Circuit (ASIC)

Field-Programmable Gate Array (FPGA)

Others

By Application:

Advanced Data Analytics

AI/ML Training and Inference

Computing

Security and Encryption

Network Functions

Others

Regional Insights

Asia Pacific dominates the global market due to explosive digital content consumption and rapid infrastructure development in countries such as China, India, Japan, and South Korea.

North America holds a significant share due to the presence of major cloud providers, AI startups, and heavy investment in advanced infrastructure. The U.S. remains a critical hub for data center deployment and innovation.

Europe is steadily adopting AI and cloud computing technologies, contributing to increased demand for accelerators in enterprise data centers.

Why Buy This Report?

Comprehensive insights into market drivers, restraints, trends, and opportunities

In-depth analysis of the competitive landscape

Region-wise segmentation with revenue forecasts

Includes strategic developments and key product innovations

Covers historical data from 2017 and forecast till 2031

Delivered in convenient PDF and Excel formats

Frequently Asked Questions (FAQs)

1. What was the size of the global data center accelerator market in 2022? The market was valued at US$ 14.4 Bn in 2022.

2. What is the projected market value by 2031? It is projected to reach US$ 89.8 Bn by the end of 2031.

3. What is the key factor driving market growth? The surge in demand for AI/ML processing and high-performance computing is the major driver.

4. Which region holds the largest market share? Asia Pacific is expected to dominate the global data center accelerator market from 2023 to 2031.

5. Who are the leading companies in the market? Top players include NVIDIA, Intel, AMD, Meta, Google, Huawei, Dell, and Cisco.

6. What type of accelerator dominates the market? GPUs currently dominate the market due to their parallel processing efficiency and widespread adoption in AI/ML applications.

7. What applications are fueling growth? Applications like AI/ML training, advanced analytics, and network security are major contributors to the market's growth.

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About Transparency Market Research Transparency Market Research, a global market research company registered at Wilmington, Delaware, United States, provides custom research and consulting services. Our exclusive blend of quantitative forecasting and trends analysis provides forward-looking insights for thousands of decision makers. Our experienced team of Analysts, Researchers, and Consultants use proprietary data sources and various tools & techniques to gather and analyses information. Our data repository is continuously updated and revised by a team of research experts, so that it always reflects the latest trends and information. With a broad research and analysis capability, Transparency Market Research employs rigorous primary and secondary research techniques in developing distinctive data sets and research material for business reports. Contact: Transparency Market Research Inc. CORPORATE HEADQUARTER DOWNTOWN, 1000 N. West Street, Suite 1200, Wilmington, Delaware 19801 USA Tel: +1-518-618-1030 USA - Canada Toll Free: 866-552-3453 Website: https://www.transparencymarketresearch.com Email: sales@transparencymarketresearch.comTop of Form

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[inspire2rise]

Samsung’s 4nm SF4X UCIe Chiplet Hits 24Gbps in First Performance Test

A Breakthrough for Samsung’s AI Chip Ambitions Samsung Electronics has taken a significant step forward in the race for AI chip dominance. On June 17, 2025, Korean outlet etnews reported that Samsung successfully completed the first performance evaluation of its UCIe prototype chip, built on the SF4X process, a 4nm derivative tailored for high-performance computing (HPC) and AI workloads.…

[skywardtelecom]

HPE Servers' Performance in Data Centers

HPE servers are widely regarded as high-performing, reliable, and well-suited for enterprise data center environments, consistently ranking among the top vendors globally. Here’s a breakdown of their performance across key dimensions:

1. Reliability & Stability (RAS Features)

Mission-Critical Uptime: HPE ProLiant (Gen10/Gen11), Synergy, and Integrity servers incorporate robust RAS (Reliability, Availability, Serviceability) features:

iLO (Integrated Lights-Out): Advanced remote management for monitoring, diagnostics, and repairs.

Smart Array Controllers: Hardware RAID with cache protection against power loss.

Silicon Root of Trust: Hardware-enforced security against firmware tampering.

Predictive analytics via HPE InfoSight for preemptive failure detection.

Result: High MTBF (Mean Time Between Failures) and minimal unplanned downtime.

2. Performance & Scalability

Latest Hardware: Support for newest Intel Xeon Scalable & AMD EPYC CPUs, DDR5 memory, PCIe 5.0, and high-speed NVMe storage.

Workload-Optimized:

ProLiant DL/ML: Versatile for virtualization, databases, and HCI.

Synergy: Composable infrastructure for dynamic resource pooling.

Apollo: High-density compute for HPC/AI.

Scalability: Modular designs (e.g., Synergy frames) allow scaling compute/storage independently.

3. Management & Automation

HPE OneView: Unified infrastructure management for servers, storage, and networking (automates provisioning, updates, and compliance).

Cloud Integration: Native tools for hybrid cloud (e.g., HPE GreenLake) and APIs for Terraform/Ansible.

HPE InfoSight: AI-driven analytics for optimizing performance and predicting issues.

4. Energy Efficiency & Cooling

Silent Smart Cooling: Dynamic fan control tuned for variable workloads.

Thermal Design: Optimized airflow (e.g., HPE Apollo 4000 supports direct liquid cooling).

Energy Star Certifications: ProLiant servers often exceed efficiency standards, reducing power/cooling costs.

5. Security

Firmware Integrity: Silicon Root of Trust ensures secure boot.

Cyber Resilience: Runtime intrusion detection, encrypted memory (AMD SEV-SNP, Intel SGX), and secure erase.

Zero Trust Architecture: Integrated with HPE Aruba networking for end-to-end security.

6. Hybrid Cloud & Edge Integration

HPE GreenLake: Consumption-based "as-a-service" model for on-premises data centers.

Edge Solutions: Compact servers (e.g., Edgeline EL8000) for rugged/remote deployments.

7. Support & Services

HPE Pointnext: Proactive 24/7 support, certified spare parts, and global service coverage.

Firmware/Driver Ecosystem: Regular updates with long-term lifecycle support.

Ideal Use Cases

Enterprise Virtualization: VMware/Hyper-V clusters on ProLiant.

Hybrid Cloud: GreenLake-managed private/hybrid environments.

AI/HPC: Apollo systems for GPU-heavy workloads.

SAP/Oracle: Mission-critical applications on Superdome Flex.

Considerations & Challenges

Cost: Premium pricing vs. white-box/OEM alternatives.

Complexity: Advanced features (e.g., Synergy/OneView) require training.

Ecosystem Lock-in: Best with HPE storage/networking for full integration.

Competitive Positioning

vs Dell PowerEdge: Comparable performance; HPE leads in composable infrastructure (Synergy) and AI-driven ops (InfoSight).

vs Cisco UCS: UCS excels in unified networking; HPE offers broader edge-to-cloud portfolio.

vs Lenovo ThinkSystem: Similar RAS; HPE has stronger hybrid cloud services (GreenLake).

Summary: HPE Server Strengths in Data Centers

Reliability: Industry-leading RAS + iLO management. Automation: AI-driven ops (InfoSight) + composability (Synergy). Efficiency: Energy-optimized designs + liquid cooling support. Security: End-to-end Zero Trust + firmware hardening. Hybrid Cloud: GreenLake consumption model + consistent API-driven management.

Bottom Line: HPE servers excel in demanding, large-scale data centers prioritizing stability, automation, and hybrid cloud flexibility. While priced at a premium, their RAS capabilities, management ecosystem, and global support justify the investment for enterprises with critical workloads. For SMBs or hyperscale web-tier deployments, cost may drive consideration of alternatives.

[giazhou1]

Discover how InnoChill's single-phase immersion cooling is revolutionizing Microsoft Azure's AI and HPC data centers with ultra-low PUE, zero water usage, and enhanced GPU performance. A sustainable solution for next-gen cloud workloads.

[techpulsecanada]

Did you know AMD's latest Instinct MI350 series powers the next generation of AI and HPC with groundbreaking 3nm technology and over 185 billion transistors? 🚀 This new lineup features the MI350X and MI355X, supporting FP4 & FP6 data types, with up to 288 GB of HBM3E memory, making it a game-changer for AI workloads. In fact, the MI355X is 35 times faster in inference than the previous MI300 series and outperforms competition with 8 TB/s memory bandwidth and 20 PFLOPs of compute performance. For tech enthusiasts and data center innovators, these advancements highlight a future where AI and high-performance computing become more powerful, scalable, and energy-efficient. Thinking about upgrading your AI infrastructure or custom PC build? Explore the potential with GroovyComputers.ca for tailor-made solutions that leverage the latest hardware innovations. Which AI or data center project would you accelerate with the new AMD Instinct series? Let us know in the comments! #AI #HighPerformanceComputing #DataCenter #AMD #GPU #ServerHardware #HPC #AIInference #TechInnovation #CustomBuilds #GroovyComputers

[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.

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[groovy-computers]

Did you know AMD's latest Instinct MI350 series powers the next generation of AI and HPC with groundbreaking 3nm technology and over 185 billion transistors? 🚀 This new lineup features the MI350X and MI355X, supporting FP4 & FP6 data types, with up to 288 GB of HBM3E memory, making it a game-changer for AI workloads. In fact, the MI355X is 35 times faster in inference than the previous MI300 series and outperforms competition with 8 TB/s memory bandwidth and 20 PFLOPs of compute performance. For tech enthusiasts and data center innovators, these advancements highlight a future where AI and high-performance computing become more powerful, scalable, and energy-efficient. Thinking about upgrading your AI infrastructure or custom PC build? Explore the potential with GroovyComputers.ca for tailor-made solutions that leverage the latest hardware innovations. Which AI or data center project would you accelerate with the new AMD Instinct series? Let us know in the comments! #AI #HighPerformanceComputing #DataCenter #AMD #GPU #ServerHardware #HPC #AIInference #TechInnovation #CustomBuilds #GroovyComputers

[sharon-ai]

Revolutionizing AI Workloads with AMD Instinct MI300X and SharonAI’s Cloud Computing Infrastructure

As the world rapidly embraces artificial intelligence, the demand for powerful GPU solutions has skyrocketed. In this evolving landscape, the AMD Instinct MI300X emerges as a revolutionary force, setting a new benchmark in AI Acceleration, performance, and memory capacity. When paired with SharonAI’s state-of-the-art Cloud Computing infrastructure, this powerhouse transforms how enterprises handle deep learning, HPC, and generative AI workloads.

At the heart of the MI300X’s excellence is its advanced CDNA 3 architecture. With an enormous 192 GB of HBM3 memory and up to 5.3 TB/s of memory bandwidth, it delivers the kind of GPUpower that modern AI and machine learning workloads demand. From training massive language models to running simulations at scale, the AMD Instinct MI300X ensures speed and efficiency without compromise. For organizations pushing the boundaries of infrastructure, this level of performance offers unprecedented flexibility and scale.

SharonAI, a leader in GPU cloud solutions, has integrated the AMD Instinct MI300X into its global infrastructure, offering clients access to one of the most powerful AIGPU solutions available. Whether you're a startup building new GenerativeAI models or an enterprise running critical HPC applications, SharonAI’s MI300X-powered virtual machines deliver high-throughput, low-latency computing environments optimized for today’s AI needs.

One of the standout advantages of the MI300X lies in its ability to hold massive models in memory without needing to split them across devices. This is particularly beneficial for Deep Learning applications that require processing large datasets and models with billions—or even trillions—of parameters. With MI300X on SharonAI’s cloud, developers and data scientists can now train and deploy these models faster, more efficiently, and more cost-effectively than ever before.

Another key strength of this collaboration is its open-source flexibility. Powered by AMD’s ROCm software stack, the MI300X supports popular AI frameworks like PyTorch, TensorFlow, and JAX. This makes integration seamless and ensures that teams can continue building without major workflow changes. For those who prioritize vendor-neutral infrastructure and future-ready systems, this combination of hardware and software offers the ideal solution.

SharonAI has further distinguished itself with a strong commitment to sustainability and scalability. Its high-performance data centers are designed to support dense GPU workloads while maintaining carbon efficiency—a major win for enterprises that value green technology alongside cutting-edge performance.In summary, the synergy between AMD Instinct MI300X and SharonAI provides a compelling solution for businesses looking to accelerate their AI journey. From groundbreaking GenerativeAI to mission-critical HPC, this combination delivers the GPUpower, scalability, and flexibility needed to thrive in the AI era. For any organization looking to enhance its ML infrastructure through powerful, cloud-based AIGPU solutions, SharonAI’s MI300X offerings represent the future of AI Acceleration and Cloud Computing.

[chemicalmarketwatch-sp]

Data Center Liquid Cooling Market Size, Forecast & Growth Opportunities

In 2025 and beyond, the data center liquid cooling market size is poised for significant growth, reshaping the cooling landscape of hyperscale and enterprise data centers. As data volumes surge due to cloud computing, AI workloads, and edge deployments, traditional air-cooling systems are struggling to keep up. Enter liquid cooling—a next-gen solution gaining traction among CTOs, infrastructure heads, and facility engineers globally.

Market Size Overview: A Surge in Demand

The global data center liquid cooling market size was valued at USD 21.14 billion in 2030, and it is projected to grow at a CAGR of over 33.2% between 2025 and 2030. By 2030, fueled by escalating energy costs, density of server racks, and the drive for energy-efficient and sustainable operations.

This growth is also spurred by tech giants like Google, Microsoft, and Meta aggressively investing in high-density AI data centers, where air cooling simply cannot meet the thermal requirements.

What’s Driving the Market Growth?

AI & HPC Workloads The rise of artificial intelligence (AI), deep learning, and high-performance computing (HPC) applications demand massive processing power, generating heat loads that exceed air cooling thresholds.

Edge Computing Expansion With 5G and IoT adoption, edge data centers are becoming mainstream. These compact centers often lack space for elaborate air-cooling systems, making liquid cooling ideal.

Sustainability Mandates Governments and corporations are pushing toward net-zero carbon goals. Liquid cooling can reduce power usage effectiveness (PUE) and water usage, aligning with green data center goals.

Space and Energy Efficiency Liquid cooling systems allow for greater rack density, reducing the physical footprint and optimizing cooling efficiency, which directly translates to lower operational costs.

Key Technology Trends Reshaping the Market

Direct-to-Chip (D2C) Cooling: Coolant circulates directly to the heat source, offering precise thermal management.

Immersion Cooling: Servers are submerged in thermally conductive dielectric fluid, offering superior heat dissipation.

Rear Door Heat Exchangers: These allow retrofitting of existing setups with minimal disruption.

Modular Cooling Systems: Plug-and-play liquid cooling solutions that reduce deployment complexity in edge and micro-data centers.

Regional Insights: Where the Growth Is Concentrated

North America leads the market, driven by early technology adoption and hyperscale investments.

Asia-Pacific is witnessing exponential growth, especially in India, China, and Singapore, where government-backed digitalization and smart city projects are expanding rapidly.

Europe is catching up fast, with sustainability regulations pushing enterprises to adopt liquid cooling for energy-efficient operations.

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Key Players in the Liquid Cooling Space

Some of the major players influencing the data center liquid cooling market size include:

Vertiv Holdings

Schneider Electric

LiquidStack

Submer

Iceotope Technologies

Asetek

Midas Green Technologies

These innovators are offering scalable and energy-optimized solutions tailored for the evolving data center architecture.

Forecast Outlook: What CTOs Need to Know

CTOs must now factor in thermal design power (TDP) thresholds, AI-driven workloads, and sustainability mandates in their IT roadmap. Liquid cooling is no longer experimental—it is a strategic infrastructure choice.

By 2027, more than 40% of new data center builds are expected to integrate liquid cooling systems, according to recent industry forecasts. This shift will dramatically influence procurement strategies, energy models, and facility designs.

Request sample report - Dive into market size, trends, and future

Conclusion: 

The data center liquid cooling market size is set to witness a paradigm shift in the coming years. With its ability to handle intense compute loads, reduce energy consumption, and offer environmental benefits, liquid cooling is becoming a must-have for forward-thinking organizations. It is time to evaluate and invest in liquid cooling infrastructure now—not just to stay competitive, but to future-proof their data center operations for the AI era.