Cisco AI Canvas: Building AI-Ready Infrastructure

Cisco AI Canvas represents a comprehensive approach to building AI-ready infrastructure that supports the entire spectrum of artificial intelligence workloads. From simple CPU-based inference to complex GPU-accelerated training, Cisco's AI infrastructure solutions provide the foundation for enterprise AI transformation.

What is Cisco AI Canvas?

Cisco AI Canvas is not a single product but rather a holistic framework that encompasses Cisco's entire portfolio of AI-ready infrastructure solutions. It includes:

• Cisco AI Pods - Pre-configured, validated AI infrastructure bundles
• Cisco UCS X-Series - Modular compute systems optimized for AI workloads
• Cisco Nexus Hyperfabric - AI-optimized networking fabric
• Cisco Validated Designs - Reference architectures for AI deployments
• Cisco Intersight - Cloud-based management platform

Key Components of Cisco AI Infrastructure

Cisco AI Pods

Cisco AI Pods are pre-configured, orderable AI infrastructure stacks that simplify deployment and reduce time-to-value. Available in four configurations:

Use Cases by Pod Size

• Small Pod: Edge inferencing, small model inference (13B-40B parameters)
• Medium Pod: RAG augmented inferencing, medium-scale deployments
• Large Pod: Large-scale RAG inferencing, complex model training
• Scale-Out Pod: Multi-model inferencing clusters, enterprise-scale AI

Cisco UCS X-Series for AI

The Cisco UCS X-Series Modular System provides the compute foundation for AI workloads with unprecedented flexibility and scalability.

Key Features

• Modular Design: Mix compute nodes, GPU accelerators, and storage in a single chassis
• Future-Proof: Midplane-free design adapts to emerging technologies
• Cloud-Managed: Simplified operations through Cisco Intersight
• High Performance: Support for latest Intel Xeon and AMD EPYC processors

AI Networking with Cisco Nexus

AI workloads demand high-performance, low-latency networking to support GPU-to-GPU communication and data movement.

Cisco Nexus Hyperfabric

Network Architecture Benefits

• Lossless Ethernet: Prevents packet loss during AI training
• Ultra-Low Latency: Sub-microsecond switching for real-time AI
• Massive Scale: Support for thousands of GPUs in a single fabric
• Automated Operations: AI-driven network optimization

YouTube Resources

Getting Started with Cisco AI Infrastructure

🎥 Cisco AI-Ready Infrastructure Overview

• Introduction to Cisco's AI infrastructure portfolio
• Key components and benefits
• Real-world deployment examples

🎥 Deploying Cisco AI Pods

• Step-by-step deployment guide
• Configuration best practices
• Performance optimization tips

Technical Deep Dives

🎥 Cisco UCS X-Series for AI Workloads

• Modular design benefits
• GPU integration and performance
• Scaling strategies

🎥 Cisco Nexus Hyperfabric Deep Dive

• AI networking requirements
• Fabric design principles
• Performance benchmarks

Customer Success Stories

🎥 Enterprise AI Transformation with Cisco

• Real customer deployments
• ROI and business outcomes
• Lessons learned

AI Workload Architecture

Validated Designs and Reference Architectures

Cisco provides comprehensive validated designs to accelerate AI deployments:

Available CVDs (Cisco Validated Designs)

1. FlexPod for Accelerated RAG Pipeline

   • NVIDIA NIM integration
   • Retrieval-Augmented Generation optimization
   • Enterprise-ready deployment

2. AI Inferencing with Intel OpenVINO

   • CPU-optimized AI inference
   • Edge deployment scenarios
   • Cost-effective AI solutions

3. GPU-Accelerated AI Training

   • Multi-GPU scaling
   • High-performance storage
   • Optimized networking

4. AI/ML Networking Blueprint

   • Lossless Ethernet design
   • GPU fabric optimization
   • Performance tuning guidelines

Management and Operations

Cisco Intersight for AI Infrastructure

Key Management Features

• Cloud-Based Platform: Access from anywhere with SaaS convenience
• Automated Provisioning: Rapid deployment of AI infrastructure
• Global Visibility: Monitor all AI infrastructure from single pane
• Predictive Analytics: Prevent issues before they impact workloads

Security Considerations for AI Infrastructure

AI workloads require robust security measures to protect sensitive data and models:

Security Framework

Performance Optimization

AI Workload Optimization Strategies

1. GPU Utilization Optimization

   • Batch size tuning
   • Memory management
   • Multi-GPU scaling

2. Network Optimization

   • RDMA over Converged Ethernet (RoCE)
   • Traffic shaping and QoS
   • Congestion control

3. Storage Performance

   • NVMe optimization
   • Data pipeline acceleration
   • Caching strategies

Getting Started with Cisco AI Canvas

Deployment Roadmap

Next Steps

1. Assess Your AI Requirements

   • Identify target AI workloads
   • Determine performance requirements
   • Evaluate existing infrastructure

2. Engage with Cisco Partners

   • Work with certified system integrators
   • Leverage Cisco design expertise
   • Access validated configurations

3. Start with Pilot Deployment

   • Begin with single AI Pod
   • Validate performance and operations
   • Plan for production scale-out

Additional Resources

Documentation and Guides

• 📖 Cisco AI-Ready Infrastructure Design Guide
• 📋 AI Pod Ordering Guide
• 🔧 UCS X-Series Configuration Guide

Training and Certification

• 🎓 Cisco AI Infrastructure Specialist Certification
• 📚 DevNet AI/ML Learning Labs
• 🏋️ Hands-on AI Infrastructure Workshops

Community and Support

• 💬 Cisco Community AI Forums
• 🆘 Technical Support and Services
• 👥 Cisco AI User Groups and Events

Cisco AI Canvas provides the foundation for your AI transformation journey. With pre-validated designs, automated management, and comprehensive support, you can accelerate your AI initiatives while ensuring enterprise-grade reliability and security.

Ready to build your AI-ready infrastructure? Contact your Cisco partner or visit cisco.com/ai to get started.