NVIDIA NCP-AIN - NVIDIA-Certified Professional AI Networking

TheCloudAI Benchmarkis designed to evaluate and establish performance baselines in AI-optimized networking environments like NVIDIA Spectrum-X. It assesses various performance metrics, including throughput and latency, ensuring that the network meets the demands of AI workloads. This benchmarking is essential for validating the benefits of Spectrum-X and for ongoing performance monitoring.​

Modern AI training (especially with LLMs) requires extremely high-speed, parallel access to large datasets. Adedicated storage fabricseparates data I/O traffic from the training compute path and avoids contention.

FromNVIDIA DGX Infrastructure Reference Architectures:

"Dedicated storage networks eliminate I/O bottlenecks by providing low-latency, high-bandwidth access to distributed storage for large-scale training jobs."

"Parallel access to datasets is key for performance, especially in multi-node, multi-GPU AI clusters."

Security (B)is important, but not the core reason for a storage fabric.

Cost (D)is typicallyincreased, not reduced, with dedicated fabrics.

The ib_write_bw tool is part of the Perftest package and is specifically designed to measure the bandwidth of RDMA write operations between two InfiniBand endpoints. It provides accurate assessments of RDMA throughput, which is crucial for verifying the performance of InfiniBand networks in high-performance computing and AI training environments.​

The NVIDIA NVUE Collection for Ansible includes pre-built roles designed to streamline automation tasks across various switch models and configurations. These roles encapsulate common network configurations, allowing for efficient and consistent deployment.​

By utilizing these roles, network administrators can:​

Apply standardized configurations across different devices.​

Reduce the complexity of playbooks by reusing modular components.​

Ensure consistency and compliance with organizational policies.​

This approach aligns with Ansible best practices, promoting maintainability and scalability in network automation.​

MLNX_OFED (Mellanox OpenFabrics Enterprise Distribution) is an NVIDIA-tested and packaged version of the OpenFabrics Enterprise Distribution (OFED) for Linux. It provides the necessary drivers and tools to support InfiniBand and Ethernet interconnects using the same RDMA (Remote Direct Memory Access) and kernel bypass APIs. MLNX_OFED enables high-performance networking capabilities essential for AI clusters, including support for up to 400Gb/s InfiniBand and RoCE (RDMA over Converged Ethernet).​

Reference Extracts from NVIDIA Documentation:

"MLNX_OFED is an NVIDIA tested and packaged version of OFED that supports two interconnect types using the same RDMA (remote DMA) and kernel bypass APIs called OFED verbs – InfiniBand and Ethernet."​

"Up to 400Gb/s InfiniBand and RoCE (based on the RDMA over Converged Ethernet standard) over 10/25/40/50/100/200/400GbE are supported."​

NVIDIA Air is a cloud-based network simulation tool designed to create digital twins of data center infrastructure, including Spectrum-X networks. It allows users to model switches, SuperNICs, and storage components, enabling the simulation, validation, and automation of network configurations before physical deployment. This facilitates Day 0, 1, and 2 operations, ensuring that network designs are tested and optimized for AI workloads.​

Reference Extracts from NVIDIA Documentation:

"NVIDIA Air enables cloud-scale efficiency by creating identical replicas of real-world data center infrastructure deployments."​

"NVIDIA Air allows users to model data center deployments with full software functionality, creating a digital twin. Transform and accelerate time to AI by simulating, validating, and automating changes and updates."​

"NVIDIA Air supports simulation of NVIDIA Spectrum Ethernet (Cumulus Linux and SONiC) switches and NVIDIA BlueField DPUs and SuperNICs as well as the NetQ network operations toolset."​

TheNVIDIA UFM Cyber-AI Platformis specifically designed to enhance security and operational efficiency in InfiniBand data centers. It leverages AI-powered analytics to detect security threats, operational anomalies, and predict potential network failures. By analyzing real-time telemetry data, it identifies abnormal behaviors and performance degradation, enabling proactive maintenance and threat mitigation.​

This platform integrates with existing UFM Enterprise and Telemetry services to provide a comprehensive view of the network's health and security posture. It utilizes machine learning algorithms to establish baselines for normal operations and detect deviations that may indicate security breaches or hardware issues.​

NetQintegrates multiple telemetry sources, includingWJH,DOCA, and notably,Behavioral Telemetry.

From theNetQ Documentation – Behavioral Telemetry Section:

"Behavioral telemetry in NetQ correlates server and application behavior with network events, offering insights into root cause analysis by detecting anomalies in protocol, path, or performance behavior."

This helps identify patterns like:

Misbehaving applications causing retransmits.

Sudden changes in traffic flows.

Latency spikes correlated with app-level issues.

It complements device-level telemetry by introducingintent-based anomaly detection, crucial for proactive operations.

Incorrect Options:

Flow telemetryandpacket captureoffer raw data but not behavioral insights.

Application telemetryis too vague and is not the term NetQ uses for this feature.

NVIDIA BlueField Data Processing Units (DPUs)are integral to securing AI infrastructures, especially in environments requiring stringent data privacy and security measures. BlueField DPUs offload and accelerate critical infrastructure tasks such as encryption, firewall enforcement, and intrusion detection, thereby isolating sensitive data paths from potential threats.​

In the context of AI workloads, BlueField DPUs enable secure and efficient data movement between GPUs and storage systems, ensuring that sensitive information, like financial data, is protected during both training and inference processes. Their integration into NVIDIA's reference architectures provides a hardware root of trust, essential for maintaining data integrity and compliance with security standards.​