872321-001 HPE Nvidia 8GB GDDR5 Tesla P4 GPU

HPE 872321-001 Nvidia 8GB GDDR5 Tesla P4 Computational Accelerator Overview

The HPE 872321-001 Nvidia 8GB GDDR5 Tesla P4 Computational Accelerator is a high-performance, energy-efficient GPU designed specifically for inference, machine learning, and high-performance computing (HPC) workloads. Built on the advanced Nvidia Pascal architecture, the Tesla P4 delivers breakthrough performance in a compact, low-power form factor. It is engineered to accelerate AI inference in data centers, cloud infrastructures, and edge computing environments while maintaining maximum energy efficiency. With 8GB of GDDR5 memory, 2560 CUDA cores, and superior parallel processing power, this accelerator provides the computational capacity necessary to power real-time analytics, video processing, and AI-driven decision-making in enterprise-level deployments.

Pascal GPU Architecture

Design and Parallel Processing

The Tesla P4 leverages Nvidia’s Pascal architecture, which offers an optimal balance between performance and power efficiency. Built on a 16nm FinFET process, the P4 GPU maximizes transistor density while reducing power draw. With 2560 CUDA cores, the GPU supports simultaneous parallel execution of multiple instructions, enabling fast and efficient computation across diverse workloads.

Optimized for Inference

The Pascal architecture introduces INT8 precision compute capabilities, which significantly enhance performance for deep learning inference workloads. This allows the Tesla P4 to achieve higher throughput when processing trained neural network models, making it ideal for applications such as voice recognition, image classification, and autonomous system control.

Advanced Instruction

Each CUDA core in the Tesla P4 is optimized for maximum instruction throughput and reduced latency. This design enables superior performance in both floating-point and integer operations, supporting frameworks such as TensorFlow, PyTorch, and MXNet for AI applications.

Scalable Data Center Integration

The P4’s architectural efficiency allows enterprises to deploy multiple GPUs in a single server without excessive power or thermal constraints, enabling scalable inference systems that meet evolving AI demands.

Memory and Data Throughput

8GB GDDR5 Memory Configuration

The HPE 872321-001 Tesla P4 features 8GB of high-speed GDDR5 memory, providing the bandwidth necessary to handle large neural networks and video datasets. This ensures minimal bottlenecking when processing multi-dimensional tensors and streaming real-time inference data.

High Memory Bandwidth

With a 256-bit memory interface and a bandwidth of 192 GB/s, the Tesla P4 ensures fast data transfer between GPU cores and memory. This is crucial for tasks that require rapid access to massive data sets, including real-time analytics, video transcoding, and online recommendation systems.

Memory and Error Correction

Although optimized for inference, the P4 includes support for memory protection mechanisms that enhance reliability in enterprise environments. The high-speed GDDR5 memory maintains data consistency across workloads, ensuring accurate results for scientific, AI, and HPC applications.

Performance and Power

Optimized for Data Center Inference

The Tesla P4 provides up to 22 TOPS (Tera Operations Per Second) of INT8 performance, enabling it to deliver exceptional inference throughput per watt. Its low 50–75W TDP (Thermal Design Power) makes it ideal for dense data center deployments where efficiency and thermal control are critical.

Accelerating Deep Learning Inference

With its INT8 and FP16 compute capabilities, the P4 efficiently executes trained neural network models for a wide range of AI-driven applications. Whether it’s language translation, visual search, or fraud detection, the Tesla P4 processes inference workloads with exceptional speed and reliability.

Energy Leadership

One of the defining characteristics of the HPE 872321-001 Tesla P4 is its low power consumption. By delivering high computational output within a 50W envelope, it allows cloud service providers and enterprises to increase performance-per-watt without costly infrastructure upgrades.

Thermal Optimization

Its passive cooling design utilizes server airflow for heat dissipation, enabling quiet and efficient operation within standard server configurations. This ensures consistent GPU performance even under sustained high workloads.

TensorRT and Deep Learning SDK

Developers can utilize Nvidia TensorRT and DeepStream SDK to optimize and deploy deep learning models on the Tesla P4. These tools streamline performance tuning, allowing organizations to achieve up to 8x faster inference performance compared to CPU-only deployments.

Framework Compatibility

The P4 supports leading deep learning frameworks including TensorFlow, Caffe, PyTorch, and ONNX Runtime. This makes it flexible for AI developers who need to deploy inference pipelines across heterogeneous compute environments.

Scalable Deployment

Enterprises can deploy hundreds of Tesla P4 accelerators in rack-mounted servers to process millions of real-time queries per second. This scalability enables data centers to meet growing demands in video analytics, personalized recommendations, and speech recognition systems.

Video and Image Processing

Next-Generation Video Transcoding

The HPE 872321-001 Tesla P4 excels in real-time video transcoding and streaming. With the integrated Nvidia NVENC and NVDEC engines, the P4 supports simultaneous decoding and encoding of multiple 1080p and 4K video streams, significantly reducing CPU overhead.

High-Density Video Processing

Each Tesla P4 can transcode up to 20 simultaneous 1080p video streams or handle 360-degree video workloads efficiently. This makes it a leading choice for cloud video platforms, surveillance analytics, and OTT streaming services that require fast, reliable performance at scale.

Integration with HPE Systems

Optimized for HPE Servers

The HPE 872321-001 Tesla P4 is fully certified and optimized for HPE ProLiant and Apollo servers. Its low-profile PCIe form factor allows easy installation into 1U and 2U rack servers, maximizing GPU density per rack without increasing power or cooling costs.

Scalability and Deployment Flexibility

Organizations can deploy multiple Tesla P4 GPUs in a single server to accelerate diverse AI workloads concurrently. This makes it ideal for edge inference applications, private clouds, and hyperscale data centers.

Enterprise-Grade Reliability

When paired with HPE infrastructure, the Tesla P4 ensures maximum uptime and consistent performance. Its passive design and thermal compliance align with HPE’s rigorous reliability standards for mission-critical environments.

Nvidia GRID and vGPU Technology

The Tesla P4 supports Nvidia GRID and virtual GPU (vGPU) technology, enabling multiple users to share a single GPU across virtualized environments. This allows cloud service providers and enterprises to deliver GPU-accelerated applications to remote users efficiently.

Enhanced Virtual Workstation Performance

By leveraging vGPU technology, organizations can deliver powerful graphical performance to thin clients, enabling remote access to 3D modeling, rendering, and AI development environments without compromising quality or responsiveness.

Multi-GPU Scalability

Multiple Tesla P4 units can operate in tandem to scale inference and graphics workloads linearly. This scalability allows system architects to design GPU clusters tailored to specific application demands.

Passive Cooling

The Tesla P4 is engineered with a passive cooling design that relies on server airflow for heat dissipation. This makes it ideal for dense data center environments where efficient cooling is essential for operational stability.

Power-Optimized Design

At just 50–75 watts, the Tesla P4 consumes significantly less power than traditional GPUs while delivering high compute density. This translates to lower operating costs and reduced environmental impact for enterprise deployments.

Temperature Monitoring and Control

Integrated thermal sensors monitor GPU temperatures and optimize performance dynamically. Administrators can use Nvidia-supplied management tools to balance workloads and prevent thermal throttling.

Quiet, Reliable Operation

The absence of active fans reduces noise and moving parts, resulting in lower maintenance and longer operational lifespan. This design ensures durability in continuous-use environments.

Software and Ecosystem

Comprehensive Software Stack

The HPE 872321-001 Nvidia Tesla P4 supports a wide ecosystem of software tools including CUDA, cuDNN, TensorRT, and DeepStream SDK. These libraries enable seamless deployment of optimized AI and video analytics applications.

Driver and API

With enterprise-grade Nvidia drivers, the Tesla P4 maintains stability across a variety of professional workloads. It supports DirectCompute, CUDA, OpenCL, and Vulkan APIs, ensuring broad compatibility across scientific and commercial applications.

Integration with Frameworks

The Tesla P4 seamlessly integrates with popular AI frameworks, providing optimized libraries for accelerated training and inference. Frameworks such as TensorFlow, PyTorch, and Caffe benefit from GPU-accelerated inference, drastically reducing latency and improving throughput.

Use Cases and Industry Applications

Cloud Inference

Cloud providers deploy the Tesla P4 to deliver high-performance inference services for clients across industries. Its energy efficiency and scalability make it ideal for hyperscale inference clusters that support voice assistants, chatbots, and recommendation engines.

Smart Video Analytics

With its ability to decode, analyze, and encode multiple video streams simultaneously, the P4 powers intelligent video analytics systems for smart cities, transportation, and retail environments.

Edge and IoT Deployment

The low-power profile of the Tesla P4 allows it to be deployed at the edge for real-time AI inference on IoT devices. It can process sensor data locally, reducing latency and bandwidth costs associated with cloud inference.

Financial and Healthcare Sectors

In finance, the Tesla P4 accelerates risk modeling and fraud detection using machine learning inference. In healthcare, it enables real-time analysis of medical imaging data, improving diagnostic accuracy and speed.