Experience the full AI infrastructure lifecycle before deploying it at scale. Our experts guide you through design best practices, implementation approaches, operational models, and real-world GPU, networking, and storage architectures so you can make confident decisions.

WHAT WE DO

Visit the ePlus AI Experience Center

Experience the full AI infrastructure lifecycle before deploying it at scale. Our experts guide you through design best practices, implementation approaches, operational models, and real-world GPU, networking, and storage architectures so you can make confident decisions.

HOW ePLUS CAN HELP

Experiencing AI Infrastructure

In partnership with Digital Realty, we provide hands-on demonstrations of the complete advanced AI infrastructure stack and the platform requirements needed to run enterprise AI. You’ll be able to explore real-world AI solutions built for AI workloads and interact with automated deployment, management, and monitoring demos. Read the digital brochure.

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Automated AI Infrastructure Provisioning

  • Full-stack Deployment: Demonstration across compute, storage, and networking
  • Infrastructure as Code: Consistent, repeatable workflows for faster time-to-value
  • Automation Tools: Scalable provisioning to reduce manual effort
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Managing Your AI Infrastructure – Intelligent Automation

  • Daily Ops Automation: Patch management, upgrades, compliance checks
  • Infrastructure Visualization: Real-time status, drift detection, remediation workflows
  • Lifecycle Efficiency: Automated maintenance and lifecycle operations
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GPU Resource Management Demo with Run:ai and SLURM

  • GPU Optimization: Dynamically schedule and isolate GPU workloads across teams and projects
  • Intelligent Scheduling: Improve utilization and reduce idle time
  • Interactive and batch workloads: Maximize throughput and flexibility
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Monitoring Your AI Infrastructure

  • Real-Time Metrics: Monitor GPU, memory, and network stats via Grafana and Prometheus
  • Alerting and Thresholds: Custom thresholds and proactive alerting
  • Automated Ticketing: Seamless integration with ServiceNow, Jira
  • Capacity Planning: Forecast trends to optimize resources
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Experience the full ML lifecycle with Weights & Biases integration

MLOps with Weights & Biases (W&B)

  • Experiment Tracking: Log hyperparameters, metrics, system stats
  • Artifact Management: Version datasets, models, and outputs
  • Model Registry: Promote models across dev/staging/production
  • Reporting and Collaboration: Auto-generated dashboards for audit trails and team sharing
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Engage with AI experts to discuss real world use cases and best practices

Advanced AI Infrastructure Use Cases

  • Dynamic GPU Management: Efficient GPU utilization with Run:ai and SLURM
  • Orchestration: Centralized scheduling via NVIDIA Base Command Manager
  • AI on Kubernetes: Scalable container-based AI infrastructure
  • High-Performance Networking: Optimized Ethernet fabric for distributed workloads
  • Scalable Storage: High-throughput systems designed for AI data pipelines
  • Hybrid and Multi-Cloud Deployments: Manage AI workloads across edge, on-prem, and cloud
  • GPU-as-a-Service: A consumption-based platform demonstrating bare metal provisioning and VM-based provisioning along with billing, orchestration, and integrated management and monitoring
WHY ePLUS

Our Unique Differentiators

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