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Mellanox MCX556A-EDAT 2-Port 100Gbs QSFP28 PCIE 4.0 x16 Network Card
- Dual 100Gb/s QSFP28 ports for ultra-high-speed networking.
- PCIe 4.0 x16 interface for superior bandwidth and low latency.
- Based on Mellanox ConnectX-5 technology.
- Supports advanced features like RoCEv2, NVMe-oF, and GPUDirect.
- Optimized for AI, machine learning, and high-performance computing (HPC) workloads.
- Provides high throughput and low latency for demanding data center applications.
- Energy-efficient design for reduced power consumption.
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Product Overview
The Mellanox MCX556A-EDAT is a cutting-edge 2-port 100Gb/s network interface card built on the ConnectX-5 family. It utilizes a PCIe 4.0 x16 interface for maximum bandwidth and low latency, making it ideal for high-performance computing, AI, and cloud environments.
Technical Information
| SKU | MCX556A-EDAT |
| Title | Mellanox MCX556A-EDAT 2-Port 100Gbs QSFP28 PCIE 4.0 x16 Network Card |
| Port Type | QSFP28 |
| Speed | 100Gb/s |
Additional Specifications
| Number of Ports | 2 |
| Interface | PCIe 4.0 x16 |
| Chipset | Mellanox ConnectX-5 |
Product Description
The Mellanox MCX556A-EDAT represents the pinnacle of high-speed networking technology, designed to meet the extreme demands of modern data centers, cloud infrastructures, and high-performance computing (HPC) clusters. Equipped with two 100Gb/s QSFP28 ports, this network card enables unprecedented data transfer rates, crucial for data-intensive applications such as artificial intelligence (AI), machine learning (ML), and big data analytics. Leveraging the latest PCIe 4.0 x16 interface, the MCX556A-EDAT provides a substantial increase in bandwidth compared to previous generations, significantly reducing latency and improving overall system responsiveness. This makes it an ideal solution for workloads that require rapid data movement and low-latency communication between compute nodes and storage. The card incorporates Mellanox's advanced ConnectX-5 technology, offering a rich set of features including support for Remote Direct Memory Access over Converged Ethernet (RoCEv2), NVMe over Fabrics (NVMe-oF), and GPU Direct. These capabilities are essential for optimizing performance in distributed computing environments, accelerating data processing, and enhancing the efficiency of AI and ML training models.
