Models & Labs · United States

AI Data Centers Drive Demand for SiC, GaN Power Semiconductors

Credited to TheElec · thelec.net · Just now

Power consumption is rising with the spread of artificial intelligence (AI) data centers, highlighting the importance of compound power semiconductors such as silicon carbide (SiC) and gallium nitride (GaN). Data centers are adopting high-voltage power networks to deliver more electricity efficiently. Conventional silicon (Si) power semiconductors have limitations in reducing power losses.

Bae Sung-beom, a senior principal researcher at the Electronics and Telecommunications Research Institute (ETRI), said Oct. 7 at the Korea Power Device Industry Association's Power Semiconductor Industry-Academia-Research K-Forum at Hotel Nongshim in Busan that the growth of the AI industry is causing power shortages worldwide. “Because it is difficult to expand power infrastructure at the pace of actual demand growth, it is becoming increasingly important to use limited energy efficiently,” Bae said.

Data centers operate large-scale computing resources such as graphics processing units (GPUs) around the clock. As AI models grow larger, the amount of GPUs and storage capacity required by data centers increases, driving up power consumption. How efficiently electricity supplied from external power grids can be delivered to servers with minimal losses is also becoming increasingly important.

Improving power efficiency requires reducing losses that occur when electricity from outside is converted to voltages that servers or GPUs can handle. NVIDIA's 800-volt direct-current (DC) data center power architecture has emerged against this backdrop. The 800V DC system distributes electricity inside data centers at a higher 800-volt DC voltage than conventional systems. It simplifies the existing power-supply architecture and transmits electricity at higher voltage, reducing losses during power conversion and wiring.

As power-supply voltages rise, the role of power semiconductors becomes more important. Improving power efficiency in high-voltage and high-power environments has limitations because designing devices to withstand higher voltages makes it more difficult to reduce power losses and heat generation.

Compound power semiconductors such as SiC and GaN are being considered as alternatives. They can operate at higher voltages and temperatures than Si and reduce losses during power conversion. Their ability to switch at high speeds also helps improve the efficiency of power-conversion equipment and enables smaller components.

Applications vary depending on voltage levels and power-conversion stages. SiC is suited to high-voltage, high-power environments. It can be used in equipment that converts electricity supplied from external power grids at thousands to tens of thousands of volts into power that can be used inside data centers. GaN offers fast switching performance at relatively lower voltage levels. It can help reduce power losses when voltage is stepped down inside servers to levels required by GPUs and other computing devices.

Reliability issues need to be addressed for commercialization. Compound semiconductors are manufactured by combining different materials to form devices. Differences in the coefficients of thermal expansion between materials can cause wafers to warp or crack. The thermal conductivity of substrates also affects device output and efficiency. The industry is researching ways to address these issues using new substrate materials and vertical device structures.

Chips & SupplyUnited States

HPE’s Gen13 Signals Enterprise AI Shift to CPU-GPU Balance

Credited to Data Center Knowledge · datacenterknowledge.com3h ago

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HPE launched AMD Epyc-powered ProLiant Gen13 servers for enterprise AI inference, agentic AI, and traditional workloads, targeting enterprise customers with varied AI needs.

HPE today (Oct. 7) announced the first batch of next-generation, AMD-powered servers designed to enable enterprises to run AI inference and agentic AI, as well as traditional enterprise workloads such as analytics and virtualization.

The company unveiled its first four ProLiant Gen13 servers, all powered by AMD’s sixth-generation Epyc 9006 SP7-series CPUs – code-named “Venice” – featuring new automated security and expanded management capabilities.

The new high-end ProLiant DL585a Gen13 server, which features up to eight GPUs and two AMD Venice processors, is designed for demanding enterprise workloads such as AI inference, agentic AI, retrieval-augmented generation (RAG) model fine-tuning, and analytics, the company said. The server, available in March 2027, will support GPUs from Nvidia, AMD, and Intel, said John Carter, HPE’s vice president of server product management, quality, and technical pursuit.

Related: HPE Interview: Why Data Center Efficiency Is Now Core to IT Decisions

“These are not the big [Nvidia Vera Rubin] NVL72 or AMD Helios scale-up type systems. This is for your enterprise-grade customer,” Carter said in an interview with Data Center Knowledge. “This is the first time we’ve done this kind of enterprise-grade AI system with an AMD head node. There is a lot of great performance improvements there, utilizing the larger memory bandwidth, higher-performance CPU.”

HPE also introduced three CPU-focused servers. The compact ProLiant DL525 server, available in November 2026, is a 1U, single-socket system featuring one AMD Venice chip with up to 256 cores, designed for AI inference and traditional enterprise workloads, such as virtualization, fraud detection, market data processing, modeling, and simulations, the company said.

The other two new servers, available in 2027, are CPU-based, rack-scale systems that offer high-density compute for very large enterprises or service providers, such as specialist cloud providers (often called neoclouds ) that have data-intensive AI or HPC workloads, Carter said. The ProLiant XD245 is a four-node, liquid-cooled system, while the ProLiant XD285 is a two-node, air-cooled system.

Analysts: Epyc-Powered ProLiants Aim at Enterprise AI

HPE’s server announcement signals that the company sees enterprise AI adoption on the horizon and wants to be aggressive in meeting enterprise needs, said analyst Matt Kimball of Moor Insights & Strategy. AMD’s sixth-generation Epyc chip is a strong processor for AI because of its high core count, memory capacity, and bandwidth, he added.

IDC research vice president Kuba Stolarski agreed, saying HPE’s four new servers provide organizations with a good mix of hardware for their workload needs.

Related: HPE Targets GPU Utilization With New AI Networking Portfolio

“Every enterprise is going to have a variety of different AI use cases. So you can’t have a one-size-fits-all from an infrastructure perspective,” Stolarski told Data Center Knowledge. “You can’t just put up the largest, most expensive GPU cluster and expect that’s going to be the best solution for every enterprise.”

How ProLiant Gen13 Systems Support Agentic AI Workflows

HPE executives and analysts said the three new CPU-focused ProLiant Gen13 servers can work alongside GPU-based servers for agentic AI workflows. For example, the CPU-focused servers can serve as a CPU head node in an agentic AI system, handling orchestration, tool calls, and retrieval, while GPU systems handle inference.

“You need a whole lot more CPUs to drive orchestration because these agents are making calls, and subagents are making calls. It’s just an ongoing traffic frenzy, and that’s what makes AMD’s Venice so interesting,” Kimball said. “That high core count and high single-threaded performance do a very good job of supporting orchestration for agentic AI.”

Security and Management: iLO 8 Automation and PQC Readiness

Chris Bradley, director of mainstream compute customer advocacy and technical enablement at HPE, said the ProLiant Gen13 systems will feature new automated security features as part of HPE Integrated Lights-Out 8, including support for self-encrypting drives and expanded post-quantum cryptography readiness.

Related: Discover 2026: HPE Bets on Hybrid Quantum-Supercomputing Architectures

New features in HPE Compute Ops Management software include low-touch onboarding and near-real-time power capacity insights, he said.

About the Author

Wylie Wong is a journalist and freelance writer specializing in technology, business and sports. He previously worked at CNET, Computerworld and CRN and loves covering and learning about the advances and ever-changing dynamics of the technology industry. On the sports front, Wylie is co-author of Giants: Where Have You Gone, a where-are-they-now book on former San Francisco Giants. He previously launched and wrote a Giants blog for the San Jose Mercury News, and in recent years, has enjoyed writing about the intersection of technology and sports.

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Models & Labs · Middle East & Africa

OpenAI in Talks With UAE Funds, BlackRock for $30 Billion Funding Round

Credited to TheElec · thelec.net · 3h ago

OpenAI is pursuing a major fundraising effort targeting sovereign wealth funds in the United Arab Emirates (UAE) and the world’s largest asset manager, BlackRock, as it pushes back its initial public offering (IPO) to next year. The company appears to be turning to the Middle East’s deep pool of oil wealth to cover massive infrastructure and technology development costs.

On Oct. 6 local time, Bloomberg reported, citing people familiar with the matter, that OpenAI is in talks with several major UAE investment funds, including Abu Dhabi-based technology investment firm MGX, to raise a total of $30 billion in a new funding round. MGX has previously participated in funding rounds for OpenAI and Anthropic. According to THE ELEC, MGX raised about $50 billion earlier this year to accelerate investment in AI infrastructure and core technologies.

OpenAI is valuing itself at about $1.4 trillion and is seeking to raise at least $30 billion. The company’s most recent funding round was in March, when it raised $122 billion at a valuation of $852 billion.

The UAE funds are discussing forming a consortium to participate in the funding round and are considering contributing as much as $10 billion of the total investment, according to people familiar with the matter. BlackRock is also in talks to join the funding round alongside the consortium. OpenAI, BlackRock, and MGX did not respond to requests for comment.

Altman has said that taking OpenAI public would expose the company to short-term stock-market valuations and could influence decisions on AI safety, calling an IPO in 2026 premature as the company focuses on technology development and responding to government regulation.

Boomberg reported that the latest funding round differs from the typical venture-capital funding process. Normally, a selected lead investor presents a valuation and proposes specific investment terms before negotiating with the company. OpenAI, however, is leveraging its dominant market position to set its pre-money valuation at $1.4 trillion and present that figure to potential investors as a fixed price.

Meanwhile, ChatGPT is currently running advertising services in more than 60 markets worldwide, with annual recurring revenue (ARR) from the business reportedly exceeding $1 billion.