AI Infrastructure Requirements

AI workloads demand high-performance, low-latency networks to process massive datasets efficiently. Ensuring optimal performance involves addressing path selection, resource utilization, and congestion challenges. Effective infrastructure is crucial to meet these needs and support the scalability of AI applications.

Telemetry Assisted Ethernet

Telemetry Assisted Ethernet dynamically adjusts network paths using real-time data analytics. By avoiding congestion and optimising data flow, it enhances network performance and reduces latency. This technology is vital for maintaining the efficiency of AI operations and ensuring timely job completion.

Impact of Network Impairments

Network impairments such as jitter, latency, and packet reordering can significantly delay AI workloads. The slowest packet can stall the entire process, impacting job completion time and overall performance. Identifying and mitigating these impairments is crucial for optimising AI infrastructure.

Calnex SNE-X, The 400GbE AI Network Performance Twin Test Solution

The Calnex 400GbE network emulator, SNE-X, is the first of its kind, providing a comprehensive solution for device and cluster testing, as well as AI network fabric twinning.

Mimic ever-changing real-world network conditions in a controlled, repeatable manner, enabling comprehensive testing and optimization to ensure that workload performance is accurately evaluated under different network technologies.

Enhance the reliability and resilience of AI infrastructure by optimizing clusters and improving job completion times.

Network Emulation Use Cases in AI Infrastructure Testing

Cluster/Device Testing
Fabric Twinning
Application Layer Testing

Comprehensive Testing of Clusters and Devices to Identify Opportunities for Performance Optimization

To improve resource utilisation purchasing additional GPUs to boost performance is often seen as the only option, however adding more compute resource does not speed up job completion time, it can actually increase issues on the network such as congestion and reduce performance.

Optimizing resources to be resilient to network failures and congestion is the best way to ensure AI workload efficiency.

Emulating the real-world high performance network conditions and introducing impairments like delay, jitter, and packet reordering, allows for the comprehensive testing of GPU clusters, highlighting opportunities for performance optimization.

AI Fabric Twinning for Network Optimization

Utilize the Calnex SNE-X  to create a performance twin of your AI network fabric. Emulate real-world network conditions, including packet delays, reordering, and error injection, to ensure robust and reliable AI model deployment.

The SNE-X enables comprehensive testing and twinning of your AI infrastructure, giving valuable insight into performance and resilience in production environments.

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Ensuring Positive User Experiences with Application Layer Testing

Calnex network emulators enable QA testing of AI-powered application platforms. Simulate diverse network conditions to test and optimise user experience before deployment.

Ensure your application performs reliably under various scenarios, including network delays, packet loss, and jitter. Calnex network emulators, identify potential issues early, guaranteeing positive user experience for end users.

The Smart Route to AI Workload Efficiency

The unique properties of AI workloads present a formidable set of challenges when deploying AI infrastructure.

Your Guide to Ensuring Application Resilience Under Any Network Conditions

Testing System and Application Readiness before Deployment: