Astera Labs Unveils Groundbreaking AI Infrastructure Solutions with Scorpio Smart Fabric Switches

$ALAB

Astera Labs, Inc. (Nasdaq: ALAB), a leader in semiconductor-based connectivity for AI and cloud infrastructure, has launched its latest innovation: the Scorpio Smart Fabric Switch portfolio. Designed to optimize AI dataflows, these switches ensure maximum performance per watt, high reliability, and easy deployment at cloud scale. Marking the debut of the industry’s first PCIe® 6 switch, the portfolio is built to enhance AI server performance in cloud-scale environments.

This innovation significantly reduces time-to-market and lowers total ownership costs for hyperscalers deploying AI platforms. The portfolio includes two product lines: the Scorpio P-Series, which supports GPU-to-CPU/NIC/SSD PCIe 6 connectivity, and the Scorpio X-Series, designed for high-bandwidth GPU-to-GPU clustering.

Jitendra Mohan, CEO of Astera Labs, highlighted the strategic focus of the Scorpio portfolio, stating, “Our Scorpio Smart Fabric Switches are engineered to maximize system utilization and uptime, meeting the unique needs of scale-out and scale-up AI applications.” The portfolio addresses the rising demand for specialized AI platforms that require more efficient, performance-driven solutions compared to traditional fabric technologies.

Industry experts have recognized the significance of this launch. Patrick Moorhead, Founder and CEO of Moor Insights & Strategy, noted that traditional PCIe switches are not optimized for AI workloads, leading to increased power consumption and longer development times. “Astera Labs has closed a critical gap in the market with the Scorpio portfolio, offering a more efficient solution for AI training and inference workloads,” Moorhead remarked.

The Scorpio Smart Fabric Switches are an expansion of Astera Labs’ Intelligent Connectivity Platform. They integrate seamlessly with the COSMOS suite for enhanced data center observability, security, and fleet management, providing deep insights into the health and efficiency of AI computing systems.

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