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  • Enterprise AI

GigaIO Raises $21M to Scale AI Inferencing Infrastructure


GigaIO Raises $21M to Scale AI Inferencing Infrastructure
  • by: Source Logo
  • |
  • July 18, 2025

GigaIO, a Carlsbad, California-based leader in scalable AI infrastructure, has raised $21 million in the first tranche of its Series B funding to meet the growing demand for AI inferencing solutions. Led by Impact Venture Capital, with participation from CerraCap Ventures, G Vision Capital, Mark IV Capital, and SourceCode Cerberus, the funding will drive production and innovation for GigaIO’s flagship products, SuperNODE™ and Gryf™.

Quick Intel

  • GigaIO secures $21M in Series B first close for AI inferencing.

  • Funding to scale SuperNODE™ and Gryf™ production and innovation.

  • Vendor-agnostic platform supports NVIDIA, AMD, Tenstorrent, and d-Matrix.

  • SuperNODE™ offers cost-effective, energy-efficient AI infrastructure.

  • Gryf™ is the world’s first suitcase-sized AI supercomputer for edge computing.

  • FabreX™ AI memory fabric ensures low-latency, scalable performance.

Scaling AI Infrastructure with SuperNODE™ and Gryf™

The $21 million investment will accelerate production of SuperNODE™, a rack-scale AI computing platform designed for cost and energy efficiency, and Gryf™, a portable, suitcase-sized AI supercomputer that brings datacenter-class power to the edge. “With SuperNODE and Gryf, we have created a new paradigm for cost-effective and power-efficient AI inferencing infrastructure,” said Alan Benjamin, CEO of GigaIO. The funding will also support new product development and expand sales and marketing to address surging demand for vendor-agnostic AI solutions.

Vendor-Agnostic Flexibility with FabreX™

GigaIO’s patented FabreX™ AI memory fabric architecture enables dynamic composition of compute, GPU, storage, and networking resources, delivering unmatched performance and cost efficiencies. Unlike traditional architectures, FabreX™ supports accelerators from NVIDIA, AMD, Tenstorrent, and d-Matrix, freeing customers from single-vendor dependency. This flexibility allows enterprises to scale infrastructure seamlessly, reducing power consumption by 30-40% while maintaining high performance.

Industry Impact and Strategic Partnerships

“GigaIO’s innovative infrastructure is precisely what businesses need to harness the transformative power of AI,” said Jack Crawford, Founding General Partner at Impact Venture Capital. The company’s partnership with d-Matrix, integrating Corsair accelerators with SuperNODE™, achieved record-setting performance in MLPerf Inference benchmarks, processing 30,000 tokens per second with minimal latency. This collaboration highlights GigaIO’s leadership in scalable, energy-efficient AI inferencing.

Future Growth and Market Expansion

GigaIO plans a second Series B close in the coming months, driven by strong interest from strategic and financial investors. With benchmarks showing 2x faster training and 83.5x lower latency compared to traditional Ethernet-based systems, GigaIO is poised to redefine AI infrastructure from edge to core. Rockefeller Capital Management’s Technology Investment Banking division served as the exclusive advisor for the transaction.

GigaIO’s $21 million Series B funding positions it to lead the AI inferencing market with flexible, energy-efficient solutions. By scaling production of SuperNODE™ and Gryf™ and leveraging FabreX™, GigaIO empowers enterprises to deploy AI workloads efficiently, meeting the demands of a rapidly evolving industry.

 

About GigaIO

GigaIO redefines scalable AI infrastructure, seamlessly bridging from edge to core with a dynamic, open platform built for every accelerator. Reduce power draw with GigaIO’s SuperNODE, the world’s most powerful and energy-efficient scale-up AI computing platform. Run AI jobs anywhere with Gryf, the world’s first suitcase-sized AI supercomputer that brings datacenter-class computing power directly to the edge. Both are easy to deploy and manage, utilizing GigaIO’s patented AI fabric that provides ultra-low latency and direct memory-to-memory communication between GPUs for near-perfect scaling for AI workloads.

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