Powering AI at Scale with HPE: AI-Ready & Hybrid Compute Solutions
Explore how HPE AI-ready infrastructure brings together ProLiant and Apollo servers, NVIDIA GPUs, and hybrid cloud services to support AI training, inference, and data-intensive workloads—validated and integrated by Catalyst.
Why AI workloads need purpose-built infrastructure
AI has moved from pilot projects to business-critical systems. Training models, serving real-time inference, and processing massive datasets all put unique pressure on infrastructure. Traditional general-purpose servers struggle with the combination of GPU density, memory bandwidth, and storage throughput these workloads expect.
HPE AI & hybrid compute solutions are designed specifically for this challenge. They combine modern CPUs, high-performance GPUs, fast networking, and flexible storage into platforms that scale—from a single node to full production clusters.
- Support for dense GPU configurations and accelerators
- Optimized for high I/O and memory bandwidth
- Built-in security and lifecycle management for hybrid environments
HPE’s approach to AI & hybrid compute
HPE’s strategy is to meet AI workloads wherever they run—on-premises, in colocation, at the edge, or as part of a hybrid cloud. The portfolio spans familiar HPE ProLiant servers for flexible compute, and specialized HPE Apollo platforms for GPU-intensive training.
Underneath, HPE pairs this hardware with software and services, including HPE GreenLake, to deliver capacity as-a-service and simplify ongoing operations.
Key pillars of HPE’s AI-ready infrastructure
- Unified compute strategy from edge to core and cloud
- Deep integration with NVIDIA, AMD, and modern storage
- As-a-service consumption via HPE GreenLake
- Security-by-design with Silicon Root of Trust
HPE ProLiant DL380 Gen11: flexible compute for AI & hybrid workloads
The HPE ProLiant DL380 Gen11 is a versatile rack server built for AI inference, data preprocessing, analytics, and everyday business applications. It balances core count, memory bandwidth, and storage flexibility—making it a strong foundation for mixed AI and non-AI workloads.
Versatile design for AI inference and data pipelines
DL380 Gen11 supports a range of CPU options, accelerators, and storage configurations. Teams can run AI inference services, ETL pipelines, and traditional applications side by side without over-committing to a single workload profile.
Security, management, and hybrid readiness
HPE embeds security into the silicon, firmware, and management stack. Integrated Lights-Out (iLO) simplifies remote administration, while integration with HPE GreenLake lets you monitor and govern capacity across sites.
HPE ProLiant DL385 Gen11: balanced power & efficiency
The HPE ProLiant DL385 Gen11 pairs high core counts with strong energy efficiency, making it ideal for virtualized environments, data analytics, and AI workloads that benefit from many CPU threads.
Built for diverse AI and data workloads
With support for large memory footprints and fast storage, DL385 Gen11 can host feature engineering workloads, model evaluation, and containerized microservices that support your AI pipeline.
Multi-GPU and storage-friendly design
Organizations can equip DL385 Gen11 with one or more GPUs and NVMe drives to accelerate inferencing, recommendation engines, or analytics dashboards without moving straight to a dedicated training system.
HPE Apollo 6500 Gen11 Plus: GPU power for AI model training
When organizations move into deep learning and large-scale model training, the HPE Apollo 6500 Gen11 Plus becomes the natural choice. It’s a dense, GPU-optimized platform built to deliver high throughput and low latency for complex AI workloads.
Designed for deep learning and HPC
Apollo 6500 Gen11 Plus is engineered for multi-GPU configurations, high-speed interconnects, and advanced cooling. It supports the frameworks your data science team already uses—including PyTorch, TensorFlow, and common HPC toolchains.
Scaling from lab to production
You can start with a single chassis and grow into a rack-level or data center-scale AI training environment over time. Consistent management tooling helps you expand capacity without redesigning the architecture from scratch.
Comparing HPE platforms for AI workloads
Each platform in the HPE portfolio plays a different role in the AI lifecycle—from pre-processing and inference to large-scale training. The table below summarizes where each system typically fits.
| Platform | Primary role | Typical workloads |
|---|---|---|
| HPE ProLiant DL380 Gen11 | General-purpose AI & hybrid compute | Inference services, ETL, analytics, line-of-business apps |
| HPE ProLiant DL385 Gen11 | CPU-heavy and mixed AI workloads | Virtualization, feature engineering, model scoring at scale |
| HPE Apollo 6500 Gen11 Plus | GPU-dense AI training & HPC | Deep learning training, large-scale experiments, simulation |
Key benefits of HPE AI-ready infrastructure
By standardizing on HPE for AI, organizations gain more than raw compute. They get a platform for continuous innovation that’s easier to manage, secure, and scale.
- Simplified lifecycle management with tools like iLO and HPE GreenLake
- Right-sized performance across training, inference, and supporting workloads
- Security by design embedded in firmware and hardware
- Hybrid-ready for on-prem, colo, and cloud-connected deployments
Real-world use cases and applications
HPE AI solutions support organizations in many industries—from research institutions to financial services, healthcare, manufacturing, and retail. Typical patterns include:
- Research & innovation: training large models, running simulations, and exploring new algorithms.
- Operational AI: powering recommendation systems, fraud detection, and predictive maintenance.
- Hybrid AI pipelines: training in the data center, deploying inference at the edge, and orchestrating it all through a hybrid control plane.
Frequently asked questions
Which HPE server should we start with for AI?
Many organizations start with HPE ProLiant DL380 Gen11 or DL385 Gen11 for inference, analytics, and hybrid workloads, then add HPE Apollo 6500 Gen11 Plus when they need dedicated training capacity.
Can HPE platforms mix AI and non-AI workloads?
Yes. ProLiant systems are well-suited to mixed workloads, letting you run AI services alongside databases, virtualization, and business applications.
How do NVIDIA GPUs fit into HPE’s AI strategy?
HPE designs its platforms to support NVIDIA GPUs and the broader software ecosystem, providing the acceleration needed for training and high-performance inference.
Do we need HPE GreenLake for AI deployments?
You can deploy HPE AI infrastructure traditionally or as-a-service through GreenLake. The as-a-service model is helpful if you want cloud-like consumption with on-prem control.
Can Catalyst help design and validate our AI architecture?
Yes. Catalyst works with your team to size, validate, and integrate the right combination of HPE platforms based on your data, models, and growth plans.
Design your HPE AI-ready infrastructure
From flexible ProLiant servers to GPU-dense Apollo platforms, we help you design and validate the HPE stack that fits your AI training, inference, and hybrid workloads.
Talk with our HPE AI team