HPE edge computing powered by ProLiant Edge Computing solutions
Power at the edge. Precision at scale. Get secure, high-performance computing wherever your data is created. HPE ProLiant edge computing solutions deliver the security, performance, and simplified management required for AI and real-time workloads across your edge locations.
Supercharge AI at the edge
The HPE ProLiant Compute edge portfolio allows you to run your edge AI reliably and scale seamlessly, from built-in security and automation to performance and integration for high performance AI Inferencing.
Strengthen security from silicon to cloud
Safeguard every edge location with continuous, built‑in protection anchored in silicon root of trust. Enable end‑to‑end integrity from manufacturing through end of life, while automated compliance, verified device identity, and granular access controls help you stay ahead of escalating cyber and physical threats.
Maximize workload performance in any environment
Run AI inference, real-time analytics, and industry-specific workloads exactly where data is created with high-performance, energy efficient, purpose-built systems. GPU-ready and ruggedized designs ensure consistent operation in harsh, remote, or space constrained sites, while reducing operational overhead.
Streamline operations across distributed locations
Manage thousands of servers with cloud‑native simplicity. Automate updates, gain predictive insights, and unify monitoring to reduce downtime and eliminate manual effort. Enable your IT teams to optimize performance—no onsite support required.
Our customers
Make AI work where your business works
Accelerate insights and action by running AI where data is created. Reduce latency, improve decision‑making, lower cloud and bandwidth costs, and strengthen data privacy through local processing. Explore how edge AI boosts efficiency across industries and learn practical steps to build a strong business case and scale securely with rugged, high‑performance edge systems.
Place every workload where it delivers the most value
Determine the right placement for every workload—edge, cloud, or hybrid—by weighing latency, data gravity, compliance, bandwidth, and operational constraints. Learn why a hybrid, edge-to-cloud model offers the best balance of performance, security, cost efficiency, and resilience, and discover how to design an architecture that accelerates real-time intelligence while maintaining centralized control.
Strengthen your edge strategy in a hybrid world
Learn how you can improve performance, security, and efficiency by placing each workload where it delivers the most value. This ESG (now Omdia) analysis shows how rugged, silicon‑rooted HPE ProLiant edge servers and unified cloud‑native management help you run AI and critical applications reliably at the edge—so you can act faster, protect data, and operate at scale with confidence.
Top industry use cases for edge computing
Banking & financial services
- Real time fraud detection and risk prevention
- Automated branch operations and analytics
- Smart ATM networks
- Branch video analytics
- Enhanced insurance claims and risk assessments
Government & defense
- Smart cities
- Disaster response and orchestration
- Tactical edge compute (SWaP‑optimized)
- Base and installation security
- Critical infrastructure monitoring
Healthcare
- Medical imaging acceleration at the edge
- Patient safety and fall prevention analytics
- Asset and OR workflow tracking
- On‑premises clinical data processing for compliance
- Access control and perimeter security
Manufacturing
- Vision‑based quality inspection
- Predictive maintenance and condition monitoring
- Digital twins and autonomous lines
- Worker safety and PPE compliance
- Energy optimization and yield analytics
Retail
- Loss prevention with vision AI
- Real‑time inventory and shelf intelligence
- Queue and flow analytics
- Smart checkout and resilient POS
- Dynamic pricing and contextual signage
Telecommunications
- vRAN / Open RAN at the edge
- Optimized spectrum allocation
- Network performance tuning
- Content delivery and caching at the edge
- Predictive maintenance
Our partners and customers
HPE ProLiant Compute—Edge server portfolio
 HPE ProLiant MicroServer |  HPE ProLiant ML30 |  HPE ProLiant ML110 |  HPE ProLiant ML350 |  HPE ProLiant DL20 |  HPE ProLiant DL110 |  HPE ProLiant DL145 |  HPE ProLiant EL8000s |  HPE Edgeline EL8000t |  HPE ProLiant Compute EL9000 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Edge workloads | Small Business IT | SMB, Remote Office, File/Web/Email Server, Point of Sale, entry AI | SMB, virtualization, databases, and office productivity, entry AI | SMB and business-critical workloads - ERP, CRM, databases, virtualization and AI | SMB, Remote Office, File/Web/Email Server, Point of Sale, entry AI | Telco, vRAN, ORAN, Centralized/ Distributed RAN | AI inference, Virtualization, Data analytics, Industrial automation, Vison analytics | Media streaming, AI inference, industrial IoT | Telco RAN, Compact Edge | Macro RAN sites |
| Form Factor | Ultra-compact | MicroTower | Mid Tower (4.5U rackable option) | Performance Tower (4U, rackable option) | 1U Rack | 1U Rack | Tabletop, Rack (2U) and Cabinet | 2U Ruggedized | 2U Ruggedized | 2U 2-Node Ruggedized |
| Processor | Intel® Xeon® 6300-series, Intel® Xeon® E and Intel® Pentium® | Intel® Xeon® E processors up to 8 cores | 4th and 5th Gen Intel® Xeon® Scalable Processors, up to 32 cores | Intel® Xeon® 6700/6500 P-series processors, up to 86 P-cores per socket | Intel® Xeon® 6300 series, Intel® Xeon® E and Intel® Pentium® Processors up to 8 cores | Intel® Xeon® 6 SoC processors with Intel® vRAN Boost | 4th Generation AMD EPYC™ processors (up to 64 cores) | 4th Gen Intel® Xeon® Scalable processor (SPR), 32 cores | 4th Gen Intel® Xeon® Scalable processor (SPR), 32 cores | Intel® Xeon® 6 SoC processors with Intel® vRAN Boost, 72 cores |
| Max Memory | Up to 128 GB DDR5 ECC | Up to 128 GB DDR5 | Up to 1.5TB DDR5 | Up to 8TB DDR5 | Up to 128GB | Up to 512 GB DDR5 | Up to 768GB | Up to 4TB DDR5 | Up to 4TB DDR5 | Up to 1TB DDR5 |
| Storage | 4 LFF NHP SATA HDD or 4 SFF NHP SSD | 4LFF or 8SFF, HPE NS204i-u Gen11 NVMe Hot Plug | 4 to 8 LFF SAS/SATA or 8 to 16 SFF SAS/SATA HDD/SDD | Up to 12x EDSFF or 8x SFF or 4xLFF drives | Up to 4+2 Hot Plug SFF or 2 LFF | 2x M.2 drives are supported on the Motherboard | 2 SFF or 6 EDSFF SATA/NVMe | 8 M.2 NVMe | 8 M.2 NVMe | 2 M.2 NVMe |
| Management | HPE iLO 6, OneView, Compute Ops Mgmt | HPE iLO 6, Compute Ops Mgmt | iLO 6 | iLO 7 | HPE iLO 6, OneView, Compute Ops Mgmt | Included—HPE iLO 7 Standard with Intelligent Provisioning (embedded), HPE OneView Standard (requires download), Compute Ops Mgmt | HPE iLO 6, Compute Ops Mgmt | iLO 6 | iLO 6 | iLO 7 |
| GPU | - | Up to one single-wide GPU | Up to 2 single-wide GPU | Up to 8 single-wide or 4 double-wide GPUs | Optional | Up to (2) Half-Height Half-Length GPUs or up to (2) 100W In-Line Accelerators | Up to one double-wide or 3 single-wide GPUs | Optional | Optional | Up to 2 HHHL GPUs |
FAQs
What is edge computing and why would I use it?
Edge computing is a distributed computing model that processes and analyzes data close to where it is created—such as factories, retail locations, vehicles, campuses, or remote sites—rather than relying solely on centralized data centers or public cloud services. By processing data locally, organizations can reduce latency, limit bandwidth usage, and maintain operations even with limited connectivity. This makes edge computing well suited for real‑time analytics, automation, and AI‑driven workloads.
Why would a business need to use edge computing?
Businesses adopt edge computing when digital initiatives intersect directly with physical operations. Manufacturing lines, retail stores, logistics hubs, hospitals, and energy sites generate large volumes of data that often must be acted on immediately. From a business perspective, edge computing improves operational efficiency, resilience, and cost control. Local processing reduces dependency on constant connectivity, limits bandwidth consumption, and supports data sovereignty requirements as edge deployments scale beyond early pilots into broader production environments.
What kind of hardware is needed for edge computing?
Edge computing hardware must operate reliably outside controlled data center environments. Unlike traditional servers, edge systems often face constraints related to space, power, cooling, acoustics, and physical security. Beyond raw performance, edge hardware must include embedded security and remote management capabilities. Because many edge locations lack onsite IT staff, platforms must support secure provisioning, remote access, firmware integrity validation, and autonomous operation to ensure consistent performance and protection at scale.
How does a ProLiant server work for edge computing?
HPE ProLiant servers extend enterprise class-compute capabilities beyond the data center to edge locations without compromising security, performance, or manageability. At the edge, ProLiant systems run the same operating systems, hypervisors, and container platforms used centrally, enabling architectural consistency across environments. Built-in security features such as silicon root of trust, secure boot, and continuous firmware validation help protect systems deployed in physically exposed locations while supporting standardized lifecycle operations.
Which HPE ProLiant server models are best suited for edge computing deployments?
HPE offers several ProLiant models specifically suited for edge deployments, depending on workload and environmental requirements. Compact rack and tower systems such as the ProLiant DL20, DL110, and DL145 are designed for space constrained locations and remote offices, while ruggedized platforms including the ProLiant DL145 and EL Series address harsher operational conditions. These platforms balance performance, energy efficiency, and environmental tolerance. For AI-enabled edge use cases, newer ProLiant generations support GPU acceleration and higher core density while maintaining power efficiency, reflecting HPE’s continued investment in edge optimized designs.
Tips for getting started with edge computing
- Focus on workloads that require local processing. Edge computing is best suited for latency, bandwidth, or reliability constrained workloads such as real-time analytics, AI inference, automation, and operational control, where local execution delivers clear performance or resilience benefits.
- Design for scale and repeatability early. Standardizing platforms, configurations, and deployment models from the outset reduces fragmentation and enables edge deployments to scale efficiently beyond initial locations.
- Make security foundational, not additive. Because edge systems are often physically exposed, security should be built into the platform itself, including device identity, secure boot, firmware integrity validation, and controlled remote access.
- Prioritize remote operations and lifecycle management. With limited onsite IT support, successful edge deployments rely on centralized visibility, automated provisioning, monitoring, updates, and lifecycle governance.
- Align edge with a broader hybrid architecture. Treating edge as an extension of a hybrid architecture spanning edge, core, and cloud enables scalability while maintaining centralized control and architectural consistency.