The HPE ProLiant DL145 Gen11 is designed to bring AI capabilities directly to the edge, where data is generated in places like hospitals, retail stores, and manufacturing plants. Instead of sending everything back to a central data center, you can process and analyze data locally for faster insights.

Key ways it supports AI at the edge include:

• Real-time AI insights: The server is built to handle AI models that need quick responses, such as image recognition, anomaly detection, or predictive analytics.
• Scalable GPU power: It supports GPU acceleration so you can start with a modest configuration and scale up as your AI workloads grow.
• Built-in security: Security features are integrated into the platform to help protect data and models running at the edge.
• Cost savings: By processing data locally, you can reduce bandwidth and cloud processing costs.
• Operational efficiency: Centralized management and automation tools help IT teams deploy, monitor, and update edge AI systems more efficiently.

Using the HPE ProLiant DL145 Gen11 for AI at the edge can help organizations reimagine how they operate in several industries:

• Healthcare: Enable faster, on-site analysis of medical images or patient monitoring data, supporting quicker decision-making and more responsive care.
• Retail: Run AI models in-store for demand forecasting, inventory optimization, and personalized customer experiences without relying solely on a central data center.
• Manufacturing: Use real-time analytics on the factory floor for quality control, predictive maintenance, and process optimization.

Across these use cases, organizations can benefit from:

• Reduced latency: Decisions are made where data is created, which helps time-sensitive applications.
• Lower operating costs: Local processing can reduce data transfer and cloud compute expenses.
• Improved reliability: Edge systems can continue operating even if connectivity to the central data center or cloud is limited.

The HPE ProLiant DL145 Gen11 is built to help organizations rethink how they deploy AI at the edge by balancing performance, security, and cost:

• Performance with scalable GPUs: The platform supports scalable GPU configurations, so you can match compute power to your AI workload needs and grow over time.
• Integrated security: Security is built into the server design to help protect data, models, and workloads running at the edge, which is critical in sectors like healthcare and retail.
• Cost and operational efficiency: By processing data locally, you can reduce network and cloud costs. Centralized management and automation tools help streamline deployment, updates, and monitoring, which can lower operational overhead.

This combination makes the DL145 Gen11 a practical option for organizations that want to deploy AI at the edge without overbuilding their infrastructure or compromising on security.