DriveNets is attending the AI Infra Summit panel on Scale-Out: Network Architectures for Accelerating Distributed AI Workloads at Scale at Tue Sept 15 at 2:20pm.

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Join the panel at AI Infra Summit 🗓️ Tue Sept 15 at 2:20pm 🎤 Scale-Out: Network Architectures for Accelerating Distributed AI Workloads at Scale Our VP of Product Mark…

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Company

DriveNets

Transformative networking solutions

Industry
Software Development
Location
Raanana, IL
Company size
501–1,000 employees

About DriveNets

DriveNets builds high-performance networking solutions for large-scale AI infrastructure and service-provider networks. DriveNets’ Network Cloud powers production networks at global operators such as AT&T and Comcast, while its AI Fabric solutions optimize the performance and time to first token (TTFT) in multi-vendor AI environments at foundation model labs, NeoClouds, and enterprises. DriveNets combines high-performance networking, end-to-end automation, and full-stack AI cluster optimization to remove network bottlenecks, maximize GPU utilization, accelerate AI cluster deployments, and improve token economics. DriveNets was founded by Ido Susan and Hillel Kobrinsky, two leaders in large-scale networking solutions for AI infrastructures and service providers. Over four funding rounds, DriveNets raised nearly a billion dollars. Its solutions are used by solutions power production networks for global tier-1 operators such as AT&T and Comcast, and scale large AI infrastructures at foundation model labs, NeoClouds and enterprises, consistently ranking #1 in trials for breadth of capabilities and solution quality. DriveNets-powered networks support more than 30% of total U.S. internet traffic, and DriveNets is engaged with over 100 Tier-1 operators and cloud-providers on large projects in North America, Asia and Europe. DriveNets AI Fabric brings that same networking scale, performance, and reliability to AI clusters at foundation labs, hyperscalers, and NeoClouds. The company’s high-performance AI fabric solutions maximize GPU utilization and accelerate AI cluster deployments through end-to-end optimization of the AI stack, resulting in higher tokens-per-second and lower cost per-token.

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