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Global Artificial Intelligence Server Market Analysis Report 2023-2029: The Role of Specialized Accelerators and Growing Complexity of Neural Networks Fueling Demand

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DUBLIN, Feb. 20, 2024 /PRNewswire/ — The “Global Artificial Intelligence Server Market (2023 Edition): Analysis By Value and Unit Shipment, Server Type (Data, Training, Inference, Others), AI Server Infrastructure, Hardware Architecture, End-use, By Region, By Country: Market Insights and Forecast (2019-2029)” report has been added to  ResearchAndMarkets.com’s offering.

The Global Artificial Intelligence Server Market is expected to generate USD 72.6 Billion by the end of 2029, up from USD 17.5 Billion in 2022. During the forecast period, 2024-2029, the Global Artificial Intelligence Server Market is expected to expand at a CAGR of 25%.

The research assesses market growth indicators, restraints, sales growth and demand risk, and other important statistics, as well as a full assessment of current and future market trends that are relevant to the market evolution.

The demand for AI servers is driven by the necessity for high computational power to train complex neural networks and execute real-time inferencing tasks. The market for artificial intelligence servers will continue to grow at a rapid pace primarily to the growing acceptance of AI-driven solutions in various industries, as well as the growing complexity and scale of AI applications.

The growth in AI server demand is driven by advancements in hardware technologies, including specialized accelerators like GPUs, TPUs, and FPGAs, optimizing performance and accelerating AI workloads. The overall shipment of Artificial Intelligence servers worldwide is witnessing significant growth, driven by the increasing integration of AI technologies across various industries.

The increasing need for processing power and hardware specifically designed for AI tasks is driving market growth. To address the demand, cloud service providers, technology companies, and businesses worldwide are investing in AI server infrastructure, which contributes to progress in accelerators and hardware designs created especially for AI.

The emergence of GPU technology has been a major driver of the AI Server Industry’s growth. Initially designed for graphics illustration, GPUs have developed into efficient accelerators for workloads related to AI and machine learning. GPUs are essential components for training complicated neural networks because of their superiority in handling matrix operations, which are essential in deep learning, and to their parallel processing capabilities.

Companies like NVIDIA, AMD, and others have been driving the constant advancement of GPU design, which has led to GPUs with increased computing capacity and unique features for AI applications. These developments allow for enhanced model complexity, quicker model training times, and better overall performance. As a result, in order to take advantage of the efficiency and speed provided by these developments, companies looking to implement AI applications are spending more and more in AI servers equipped with the most advanced GPUs.

Moreover, the need for AI servers with high-performance GPUs has increased due to the growth of AI workloads in a variety of industries, including healthcare, finance, and autonomous cars. As a result, the development of the industry is closely related to the continuous advancement of GPU technology, which is still a major factor.

Global Artificial Intelligence Server Market: Historic and Forecast (2019-2029)

Impact Analysis of Macro Economic Factors on Artificial Intelligence Server MarketGlobal Artificial Intelligence Server Market: AI Semiconductor MarketGlobal Artificial Intelligence Server Market: Regular & GPU Server marketGlobal Artificial Intelligence Server Market: BOM Cost ComparisonGlobal Artificial Intelligence Server Market: Chipset Sales & ComparisonGlobal Artificial Intelligence Server Market: AI Servers Growth & China AI server MarketGlobal Artificial Intelligence Server Market: DashboardGlobal Artificial Intelligence Server Market: Market Value Assessment, 2019-2029 (USD Billion)Global Artificial Intelligence Server Market: Market Volume Assessment, 2019-2029 (in Million Units)Average Price Analysis of Artificial Intelligence Server GloballyImpact of COVID-19 on Artificial Intelligence Server MarketGlobal Artificial Intelligence Server Market Segmentation: By Server TypeGlobal Artificial Intelligence Server Market, By Server Type OverviewGlobal Artificial Intelligence Server Market Size, By AI Data Server, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By AI Training Server, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By AI Inference Server, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Other Server Types, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Segmentation: By AI Server InfrastructureGlobal Artificial Intelligence Server Market, By AI Server Infrastructure OverviewGlobal Artificial Intelligence Server Market Size, By Cloud, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By On-premise, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Edge, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Segmentation: By Hardware ArchitectureGlobal Artificial Intelligence Server Market, By Hardware Architecture OverviewGlobal Artificial Intelligence Server Market Size, By GPU Servers, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By ASIC Servers, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By FPGA Servers, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Other Server Architecture, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Segmentation: By End-useGlobal Artificial Intelligence Server Market, By End-use OverviewGlobal Artificial Intelligence Server Market Size, By IT & Telecommunication, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Transportation and Automotive, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By BFSI, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Retail and Ecommerce, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Healthcare and Pharmaceutical, By Value, 2019H-2029F (USD Billion & CAGR)Global Artificial Intelligence Server Market Size, By Industrial Automation, By Value, 2019H-2029F (USD Billion & CAGR)

Competitive Positioning

Companies’ Product PositioningMarket Position MatrixMarket Share Analysis of Artificial Intelligence Server Market

Company Profiles

Nvidia CorporationHuawei Technologies Co., Ltd.Hewlett Packard EnterpriseIBM CorporationDell Technologies Inc.Fujitsu LimitedZTE CorporationSuper Micro Computer, Inc.InspurGUC

For more information about this report visit https://www.researchandmarkets.com/r/2un1o

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ResearchAndMarkets.com is the world’s leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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SOURCE Research and Markets

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Safetyfirst Systems, LLC Provides Notice of Data Security Event

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PARSIPPANY, N.J., July 23, 2026 /PRNewswire/ — Safetyfirst Systems, LLC (“SFS”) is providing notice of a data security event that may involve information relating to certain individuals. While SFS is not aware of any misuse of information associated with this event, it is providing notice to potentially affected individuals out of an abundance of caution.

On January 19, 2026, SFS identified suspicious activity involving a limited portion of its server environment. Upon discovering the activity, SFS quickly took steps to secure its systems, notified federal law enforcement, engaged leading third-party forensic specialists, and performed a detailed investigation into the nature, scope, and impact of the activity. The investigation determined that an unauthorized actor accessed and/or acquired certain files from limited SFS systems between January 16, 2026, and January 19, 2026. SFS then conducted a comprehensive review of the affected files to determine what information may have been involved and identify the individuals to whom the information relates. The review has recently concluded, and SFS is providing this notification to potentially impacted individuals out of an abundance of caution. Although the types of information vary by individual, the affected information may include names, Social Security numbers, and driver’s license numbers.

Protecting the privacy and security of the information entrusted to SFS is a responsibility the company takes very seriously. In response to this event, SFS promptly strengthened security measures, continues to enhance its technical safeguards and monitoring capabilities, and is reviewing existing policies and procedures to further protect against similar incidents in the future. SFS is also providing notice to potentially affected individuals and, where required, appropriate regulatory authorities.

Although SFS is unaware of any misuse of personal information impacted by this event, individuals are encouraged to remain vigilant against events of identity theft by reviewing account statements, explanation of benefits, and monitoring free credit reports for suspicious activity and to detect errors. Any suspicious activity should be reported to the appropriate insurance company, health care provider, or financial institution.

Individuals seeking additional information regarding this event can contact SFS’s dedicated assistance line at 1-833-289-5523 between the hours of 7:00 a.m. to 7:00 p.m. Eastern time, Monday through Friday, excluding holidays. Individuals may also write to SFS at PO Box 101, 3299 US Highway 46, Parsippany, NJ 07054-9998.

 

View original content:https://www.prnewswire.com/news-releases/safetyfirst-systems-llc-provides-notice-of-data-security-event-302831894.html

SOURCE Safetyfirst Systems, LLC

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Sunrate and Mastercard Release White Paper on Agentic AI and the Future of B2B Global Payments

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SHANGHAI, July 24, 2026 /PRNewswire/ — Sunrate, the global payment and treasury management platform, and Mastercard, a global technology company in the payments industry, unveiled a joint white paper, Beyond Automation: Defining Agentic Global Payments, at the 2026 World Artificial Intelligence Conference (WAIC).

Among the first reports in the payments industry to examine the impact of Agentic AI on B2B cross-border payments, the white paper provides a comprehensive framework for understanding how AI agents are reshaping enterprise payment operations. It proposes that cross-border payments are evolving beyond digitisation and automation into a new stage: Autonomy—where AI agents with reasoning, planning, and execution capabilities can independently orchestrate and optimise end-to-end payment and treasury workflows within defined governance frameworks.

As businesses expand across borders, B2B cross-border payments continue to be constrained by fragmented workflows, disconnected systems, foreign exchange inefficiencies, rising compliance requirements, and complex reconciliation processes. While traditional automation improves individual tasks, the white paper demonstrates that Agentic AI represents a fundamental shift by enabling intelligent agents to coordinate entire payment journeys across systems, counterparties, and approval workflows.

Drawing on Sunrate’s global payment infrastructure and AI-native product capabilities, together with Mastercard’s expertise in secure payment networks and data intelligence, the white paper defines Agentic Global Payments — a new category of AI-native global payment infrastructure built to automate and manage complex enterprise workflows.

The report identifies 16 major pain points across the B2B payment lifecycle and outlines 13 high-value AI use cases spanning supplier onboarding, accounts payable and receivable, virtual commercial cards, payment routing, foreign exchange management, compliance screening, fraud detection, reconciliation, and conversational operational support. It also demonstrates how AI agents can automate complex workflows—from extracting information across multiple document formats and conducting compliance checks to initiating payments, optimising FX execution, and completing reconciliation—while operating within enterprise governance and control frameworks.

The white paper further highlights that trusted adoption of agentic payments depends on more than technological capability. It identifies governance, transparency, security, and ecosystem collaboration as essential foundations for enterprise deployment, supported by frameworks such as Know Your Agent (KYA), payment tokenisation, auditability, and cross-industry interoperability.

Sunrate.AI portfolio currently includes the Payment Agent, FX Agent, Compliance Agent, Onboarding Agent, and Chat Agent, designed to help enterprises automate and optimise critical payment and treasury processes while maintaining compliance and operational control.

Mastercard has also been actively building the foundations for trusted agentic commerce – combining AI capabilities with verifiable authorisation, clear accountability and proven payments security. Its work in this area, including Agent Pay (alongside Agent Pay for Machines) and Verifiable Intent, are proof points in how Mastercard is enabling AI to participate in commerce safely and transparently. 

“Our mission is to make global payments seamless, compliant, and intelligent,” said Paul Meng, Co-founder and CEO of Sunrate. “As businesses continue expanding internationally, AI agents will fundamentally reshape how enterprises manage global payments—enabling smoother capital flows, reducing operational friction, and embedding real-time intelligence into every payment decision. This white paper represents an important step in helping the industry understand how Agentic AI can be deployed responsibly at enterprise scale.”

“Agentic commerce is changing how businesses make and execute payment decisions, but speed without accountability creates new categories of risk,” said Anouska Ladds, Executive Vice President, Commercial & New Payment Flows, Asia Pacific, Mastercard. “As AI starts to act on behalf of businesses, autonomous payment decisions need a clear, auditable chain of identity, intent and action. That’s what allows organisations to delegate with genuine confidence — and what will determine whether agentic commerce scales past pilots.”

Released under WAIC 2026’s theme, “Intelligent Partners, Co-creating the Future,” the white paper provides business leaders with practical guidance on adopting AI-driven payment capabilities, covering implementation approaches, governance considerations, and real-world enterprise applications.

By combining Sunrate’s expertise in global payments and treasury management with Mastercard’s trusted payment infrastructure and network capabilities, the collaboration reflects a shared commitment to accelerating the next generation of intelligent, secure, and autonomous B2B global payments.

Click here to check the white paper.

About Sunrate

Sunrate is a leading global payment and treasury management platform for businesses worldwide. Founded in 2016, Sunrate has enabled companies to operate and scale both locally and globally in 190+ countries and regions with its cutting-edge infrastructure, global network, and unified solutions.

Sunrate operates through offices across key markets, including Singapore, Kuala Lumpur, Jakarta, Hong Kong, Shanghai, and London. The company partners with the top global financial institutions, such as Citibank, Standard Chartered, Barclays, J.P. Morgan. Sunrate is also the principal member of Mastercard and Visa. To learn more about Sunrate, visit https://www.sunrate.com/.

About Mastercard

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. 

www.mastercard.com

SOURCE Sunrate

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data able to stay onshore inside their own network

SYDNEY, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that need operational data to stay onshore, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model can run on the customer’s own hardware, keeping operational data onshore.”

“For teams across Australia, New Zealand, and Singapore, two things matter: keeping data onshore, and getting answers when a job fails after hours,” said Shayne Cooper, Account Executive for APAC at JAMS Software. “JAX and JAMS MCP address both. The model can run on the customer’s own hardware, and the answer arrives in plain language at the moment it is needed.”

JAX and JAMS MCP are available now to all JAMS Web customers across Australia, New Zealand, and Singapore, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software
Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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SOURCE JAMS Software

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