Intel E830-CC-QSFP56 200GbE Dual-Port Ethernet Adapter

The Intel E830-CC-QSFP56 is a next-generation network interface card delivering dual 200GbE ports via QSFP56-DD connectors, launched in Q3 2024 to address the emerging requirements of large-scale AI infrastructure deployments. The adapter leverages Intel's latest silicon architecture to provide ultra-low latency, high packet processing throughput, and intelligent offload capabilities essential for AI training fabrics, inference clusters, and high-performance distributed storage systems. With aggregate bandwidth capacity of 400GbE, the E830-CC-QSFP56 is engineered for disaggregated data center architectures where compute, storage, and memory are distributed across interconnected nodes requiring deterministic, low-jitter networking performance.

The adapter implements advanced hardware acceleration features including remote direct memory access (RDMA) over Converged Ethernet (RoCE v2), in-network computing capabilities for collective communication primitives, and intelligent packet scheduling to minimize tail latencies in all-to-all communication patterns common in distributed AI workloads. The dual-port configuration allows for flexible deployment in high-availability topologies, redundant fabric designs, and multi-tier network architectures within hyperscale environments. Native support for modern encapsulation standards, adaptive flow control, and dynamic congestion management ensures efficient utilization of available bandwidth while maintaining predictable performance under sustained high-throughput conditions.

The E830-CC-QSFP56 is optimized for PCIe Gen5 integration, delivering significantly reduced latency and maximum host-to-network throughput compared to prior-generation adapters. The card is compatible with standard server form factors and integrates seamlessly with leading orchestration platforms, container environments, and distributed machine learning frameworks deployed in production AI clusters.

Ideal for

• Large-scale distributed AI model training clusters requiring ultra-low-latency, high-bandwidth interconnects between compute nodes and shared storage systems
• Disaggregated GPU and accelerator pools serving multiple inference workloads simultaneously with deterministic east-west fabric networking
• High-performance NVMe-over-Fabrics (NVMe-oF) storage systems requiring sub-microsecond latency and sustained multi-terabit throughput
• Collective communication acceleration for distributed deep learning frameworks performing synchronized gradient aggregation, model updates, and checkpoint operations across nodes
• Real-time analytics and data streaming pipelines ingesting petabyte-scale datasets with millisecond-latency requirements and strict bandwidth commitments
• Multi-tenant cloud environments isolating AI workload traffic through advanced QoS, traffic shaping, and virtual network segmentation capabilities

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.