867620-001 HPE Nvidia TESLA P100 SMX2 16GB Module.

Key Features & Highlights

• GPU Architecture: NVIDIA Pascal-based Tesla P100 silicon for high double-precision and single-precision throughput.
• Memory: 16GB high-bandwidth onboard memory suitable for large models and data sets.
• Form Factor: SMX2 module tailored for HPE enclosure and server integration.
• Interconnect: High-speed internal interconnects and support for NVLink-like topologies depending on system architecture.
• Target Workloads: HPC, AI training, AI inference acceleration, data analytics, virtualization, and GPU-accelerated databases.
• Enterprise Support: HPE-qualified hardware and firmware integrations, ensuring compatibility with HPE systems and support channels.

Technical Specifications 

Compute & Performance

The Tesla P100 excels at both single-precision (FP32) and double-precision (FP64) floating-point operations, making it suitable for scientific simulations and machine-learning model training. With thousands of CUDA cores, optimized memory hierarchy, and specialized instruction throughput, the P100 delivers large gains over CPU-only configurations and earlier GPU generations.

Memory & Bandwidth

The module’s 16GB of high-bandwidth memory provides the capacity and throughput necessary to process large matrix multiplications, dense tensor computations, and memory-intensive HPC kernels. The memory subsystem is optimized for streaming large data blocks and sustaining long-running compute jobs without memory thrashing.

Form Factor & Integration

The SMX2 module form factor indicates a blade-style GPU designed to plug into compatible HPE server chassis. This compact integration allows datacenter operators to deploy multiple GPUs in a small physical footprint while maintaining enterprise-grade cooling and power delivery.

Designed For — Primary Use Cases

High Performance Computing (HPC)

Research institutions and engineering teams use this module to accelerate fluid dynamics, finite element analysis, molecular dynamics, seismic modelling, and climate simulation. The Tesla P100’s strong FP64 performance accelerates workloads where numerical precision matters.

Artificial Intelligence & Deep Learning

The P100’s parallel matrix compute capabilities make it an excellent candidate for training deep neural networks, especially where FP32 and mixed-precision training offer speed improvements. Frameworks such as TensorFlow, PyTorch, and MXNet commonly benefit from GPU acceleration. The 16GB memory allows larger mini-batches and larger model architectures compared to smaller-memory cards.

Data Analytics & Database Acceleration

GPU-accelerated analytics (for example, GPU databases, real-time streaming analytics, and ETL pipelines) benefit from massively parallel processing for sorting, indexing, and query execution. Enterprises that need sub-second responses on large datasets can leverage GPU offload to reduce latency and free CPU cores for orchestration.

Compatibility & System Integration

The HPE 867620-001 module is an OEM-integrated product intended for deployment in HPE servers and blade enclosures that support SMX2-style GPU modules. When planning an upgrade or purchase, always verify chassis firmware, BMC, and BIOS levels to ensure compatibility with the module, and consult HPE documentation for validated server models and enclosure fitment.

System Requirements & Recommendations

• Ensure sufficient power budget and appropriate power supplies in the target chassis.
• Confirm airflow and cooling capacity—GPU modules increase thermal load significantly compared with CPU-only configurations.
• Validate firmware and driver stacks: HPE system firmware and NVIDIA GPU drivers should be in supported versions as per HPE release notes.
• Check virtualization platform compatibility (VMware, Red Hat Virtualization, Citrix, etc.) if running GPU-accelerated VMs or containers.

Performance Tuning & Optimization

Memory Management

To maximize throughput, profile memory access patterns and batch sizes. Use techniques such as overlapping compute and data transfer, pinned memory, and asynchronous kernel launches to hide memory latency. For deep learning, experiment with mixed-precision training where supported to improve throughput without sacrificing model accuracy.

Thermal & Power Tuning

Monitor GPU temperature and power draw using vendor tools. In densely packed configurations, balance the number of active GPUs per chassis to maintain thermal headroom. Some environments use server-level fan curves and power capping features to achieve consistent performance while staying within facility power limits.

Parallelism & Scalability

When scaling across multiple P100 modules, use job schedulers and orchestration frameworks that understand GPU resource allocation (Slurm, Kubernetes GPU operator, NVIDIA Cluster Manager, etc.). For distributed training, ensure interconnects and network topologies are configured to minimize collective communication overhead.