Cisco Announces New Silicon One G300, Advanced Systems and Optics to Power and Scale AI Data Centers for the Agentic Era

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• Hyperscale and neocloud customers need networking that matches GPU density, and G300 silicon enhances this with industry-leading buffers, power efficiency, and 1.6T port density

  80% confidence

• 100% liquid cooled systems enable nearly 70% energy efficiency improvement and deliver the same bandwidth in a single system that would previously have required 6 prior generation systems

  80% confidence

• Cisco N9000 series powered by G300 Silicon One for breakthrough 1.6T scale-out performance dramatically expands what's possible for AI infrastructure

  80% confidence

• 800G LPO reduces optical module power consumption by 50% compared to retimed optical modules and overall switch power by 30%

  80% confidence

• At AI-factory scale, performance is defined by how tightly the network and data layer work together

  80% confidence

• AI at scale demands open, standards-based networking that customers can deploy with confidence across diverse environments

  80% confidence

• Back-end scale-out networks will rapidly move to 1.6T and be a key driver to push the Ethernet data center switch market above $100B a year

  80% confidence

• Cisco Silicon One G300 enables every customer to fully utilize their compute and scale AI securely and reliably in production

  80% confidence

• Cisco's N9364E-SP2R-X and N9364F-SG3 switches with operational flexibility across NX-OS and Cisco ACI position enterprises and neoclouds to embrace high performance platform-driven innovation

  80% confidence

• Sharon AI selected Cisco Nexus Hyperfabric because it delivers a comprehensive, end-to-end infrastructure that seamlessly integrates networking, GPU, and storage

  80% confidence

• Maximizing GPU utilization, improving energy efficiency, and simplifying operations are critical to realizing real economic value from AI at scale

  80% confidence

• Cisco N9000 with G300-powered Nexus and Nexus One with 102.4 terabits capacity and industry's largest on-chip buffer is the fastest Cisco has moved

  80% confidence

• Intel Gaudi 3 AI accelerator combined with Cisco Nexus 9000 switching delivers an optimized, open solution for building cost-efficient LLM inference clusters at scale

  80% confidence

• Cisco is building the foundation for the future of infrastructure, supporting every type of customer from hyperscalers to enterprises as they shift to AI-powered workloads

  80% confidence

• Intelligent Collective Networking delivers 33% increased network utilization and 28% reduction in job completion time versus simulated non-optimized path selection

  80% confidence

• As AI training and inference scales, the network becomes part of the compute itself and must deliver scalable bandwidth and reliable, congestion-free data movement

  80% confidence

• NetApp AFX disaggregated storage and Cisco's G300 based N9000 systems with 102.4T switching enable data to reach GPUs at the speed organizations need to power innovation

  80% confidence

• Cisco is spearheading performance, manageability, and security in AI networking by innovating across the full stack from silicon to systems and software

  80% confidence

• Network architecture is becoming a defining constraint on performance, cost, and sustainability as AI adoption moves beyond hyperscalers

  80% confidence

• N9000 series with Silicon One G300 NPU gives du Tech the 100Tbps capacity and efficiency needed as they move into the 1.6T Ethernet era to support AI and cloud growth

  80% confidence

• Networking has been the fundamental constraint to scaling AI, and at 102.4T scale networking directly determines how much AI compute can actually be utilized

  80% confidence

• Silicon One G300 significantly boosts network utilization so more GPU tokens are generated per hour, reshaping economics and performance expectations for AI data centers

  80% confidence

• VAST AI Operating System unifies ingest, retrieval, vector search, and real-time analytics, removing data-path bottlenecks that starve GPUs and stall jobs

  80% confidence

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