Super Micro Computer Inc plans to begin shipping its “Data Center Building Block Solutions” (DCBBS) based on Nvidia Corp’s Vera Rubin NVL72 platform in the second half of this year, after delivering about 8,000 GB300-based systems in the first quarter of this year, the company said yesterday.
The company’s DCBBS offers significant advantages in time-to-market and deployment speed, supported by a development cycle of about 20 months from concept and innovation through design, manufacturing and delivery, Super Micro Computer cofounder and CEO Charles Liang (梁見後) told a news conference in Taipei.
The company has delivered the products to more than 30 customers worldwide, including some of the world’s largest gigawatt-scale data centers, he said.
Photo: CNA
Super Micro Computer offers a broad product portfolio, spanning large AI data centers to small enterprise AI systems, Liang said.
The company also invests in software tools to help customers develop agentic AI applications and has expanded capacity to meet customers’ needs, he said.
Over the past seven months, Super Micro Computer has added eight production facilities in the US, Liang said.
In Taiwan, a new facility is to begin operations in October, followed by another that is expected to be completed before summer next year to support demand in Taiwan, Asia and Europe, he said.
The company runs a facility in Malaysia that serves customers in Southeast Asia and parts of Europe, and could also support production of system-level and rack-scale solutions for the US market, he said.
In addition, the company continues to expand its operations in the Netherlands to provide system-level, rack-scale and data-center-scale direct liquid cooling solutions for Europe, Liang added.
With its capacity expansion, the company expects to maintain strong growth over the next several years, after posting about 80 percent annual revenue growth in the past few years, he said.
The AI hardware market is expected to maintain annual growth of more than 50 percent over the next several years, as AI applications expand beyond large-scale data centers to smaller data centers, on-site AI systems and even standalone AI devices, Liang said.
Demand for agentic AI and inference computing is expected to spread across industries, including education, finance, manufacturing and transportation, he said.
Amid robust growth in agentic AI applications, the company’s competitive advantages lie in lower power consumption, reduced water usage and faster deployment capabilities, Liang said.
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