876337-001 HPE Nvidia 16GB Tesla V100 SXM2 Computational Accelerator

Overview of HPE 876337-001 NVIDIA 16GB Tesla V100 SXM2

The HPE 876337-001 NVIDIA 16GB Tesla V100 SXM2 Computational Accelerator represents a breakthrough in high-performance computing, specifically engineered for enterprise-grade servers, advanced AI frameworks, scientific workloads, and large-scale data processing environments. Equipped with 16GB of high-bandwidth HBM2 memory and powered by NVIDIA’s Volta architecture, this accelerator enables remarkable computational throughput for next-generation workloads such as AI deep learning, neural network training, molecular modeling, seismic analysis, engineering simulations, autonomous systems, and large-scale virtualized GPU environments.

Volta Architecture and Advanced Compute

At the core of the HPE 876337-001 Tesla V100 SXM2 is the NVIDIA Volta GV100 GPU architecture, which introduces revolutionary enhancements in tensor processing, FP64 and FP32 operations, and mixed-precision computing. The architecture is optimized for deep learning models, HPC simulations, and data analytics workflows, offering exceptional parallel performance and energy-efficient processing. By leveraging over 5,000 CUDA cores and Tensor Cores dedicated to AI acceleration, the Tesla V100 SXM2 significantly reduces computation times for complex workloads, accelerating deep neural networks and enabling faster insights from massive datasets.

Tensor Core Acceleration for Deep Learning

The integration of Tensor Cores provides substantial performance gains for deep learning operations, enabling accelerated matrix multiplication and significantly faster neural network training. This makes the Tesla V100 SXM2 ideal for machine learning frameworks such as TensorFlow, PyTorch, and MXNet. The Tensor Core enhancement allows users to reduce model training time, perform more frequent iterations, and optimize neural network performance for natural language processing, image recognition, generative AI, and scientific research applications where accelerated computing is crucial.

Enhanced CUDA Core Performance

The Tesla V100 SXM2 is built with thousands of CUDA cores delivering outstanding compute power for HPC workloads. The enhanced architecture ensures that FP64 double-precision computations are executed efficiently, serving applications in weather modeling, seismic simulations, astrophysics, and quantum chemistry. Single-precision workflows also benefit from the architecture’s ability to process parallel tasks efficiently, supporting workloads such as signal processing, high-performance rendering, and large-scale data manipulation in enterprise server deployments.

Parallel Processing

The massive parallel architecture allows multiple operations to run concurrently without performance bottlenecks. This enhances the GPU’s capabilities in network training, simulation accuracy, numerical modeling, and real-time processing of large scientific datasets. The HPE 876337-001 Tesla V100 ensures that computational workloads are balanced efficiently across its core architecture, supporting high throughput and minimizing latency during execution.

High-Bandwidth HBM2 Memory for Data-Intensive Workloads

Equipped with 16GB of high-bandwidth HBM2 memory, the Tesla V100 SXM2 delivers exceptional memory throughput crucial for AI model training, simulations, video processing, and large distributed computing environments. The memory operates on a wide bus with extremely high transfer rates, allowing GPUs to access massive datasets quickly while maintaining consistent performance even under demanding conditions. HBM2 technology ensures low-latency access to data, supporting high-speed processing for workloads that require rapid retrieval of complex information such as molecular simulations, rendering pipelines, large neural network datasets, and enterprise AI systems.

Scalability for Multi-GPU Configurations

The HBM2 memory design allows the Tesla V100 to maintain consistent performance in multi-GPU deployments. When configured within HPE servers supporting SXM2 GPU modules, multiple V100 accelerators can run parallel tasks with synchronized memory access, enabling near-linear scaling for high-performance computing tasks. Data center workloads that involve massive datasets benefit from this scalability, ensuring high throughput and minimal bottlenecks across GPU clusters.

Workload Acceleration with High Memory Bandwidth

The high bandwidth ensures that data-intensive processes such as AI inference, 3D rendering, and scientific research simulations operate smoothly without delay. Memory-intensive tasks involving large matrices, multi-parameter models, and high-resolution datasets can exploit the HBM2 bandwidth to achieve faster precise computation, optimized rendering cycles, and rapid testing cycles for AI development.

SXM2 Form Factor

The SXM2 form factor provides a highly efficient thermal interface and superior bandwidth compared to PCIe GPU designs. This configuration allows for higher power envelopes and improved cooling efficiency, which ensures sustained performance during long-duration computations. The HPE 876337-001 Tesla V100 SXM2 benefits from enhanced electrical and thermal connectivity, enabling continuous operation under heavy workloads without throttling. Servers that support SXM2 GPUs can stack multiple accelerators, benefiting from optimized heat dissipation and extended compute capacity for large-scale workloads.

Interconnect Interface

The SXM2 connector supports advanced interconnect technologies that offer faster communication between GPUs, enhancing performance in distributed training or multi-GPU HPC tasks. This high-speed interface minimizes latency and improves overall compute synchronization, enabling efficient cluster deployment for AI and scientific research platforms.

Sustained Operation

SXM2-based GPUs like the Tesla V100 are engineered to maintain stable frequencies even during the most demanding workloads. The enhanced cooling structure enables long-term execution of complex models, ensuring that GPU performance remains consistent over extended computation cycles. Data centers and enterprises requiring 24/7 computational output benefit greatly from this thermal optimization.

Deep Learning Applications

The HPE 876337-001 NVIDIA Tesla V100 SXM2 excels in deep learning environments, significantly reducing training time for large neural networks and enabling real-time inference. Its AI-optimized architecture and tensor compute units accelerate the execution of convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformer-based models. Industries leveraging AI for automation, analytics, medical imaging, predictive modeling, and large dataset exploration can benefit immensely from V100 acceleration.

Training Large Neural Networks

Deep learning models often require immense computing power and large datasets for training. The Tesla V100 enables reduced training cycles and more efficient multi-epoch execution, speeding up experimentation and model refinement. Its ability to handle mixed-precision computations ensures optimal balance between speed and accuracy for AI workflows.

Inference Acceleration

Beyond training, the Tesla V100 SXM2 boosts inference performance for AI applications, enabling real-time predictive analytics, fraud detection, autonomous decision-making, and natural language interpretation. Combined with enterprise-grade HPE server architecture, these capabilities create a robust environment for deploying production-ready AI solutions.

High-Performance Computing and Scientific Simulation

The HPE 876337-001 Tesla V100 SXM2 is widely used in high-performance computing environments where scientific simulations require immense computational capability. Its double-precision performance makes it ideal for cloud-based HPC clusters, engineering simulations, molecular modeling, climate analysis, particle physics, and geophysical research. The GPU’s ability to execute trillions of floating-point operations per second allows researchers and engineers to run complex simulations with increased accuracy and reduced runtime.

Simulation Accuracy and Numerical Computation

Scientific workloads that demand high computational precision benefit from the V100’s FP32 and FP64 processing capabilities. Applications such as quantum chemistry, computational fluid dynamics, and materials science simulations run more efficiently with accelerated GPU compute resources, enabling quicker innovation cycles and deeper computational insights.

Data-Intensive Research Workflows

The Tesla V100 SXM2 is capable of processing large datasets common in scientific research, including time-series data, multi-dimensional datasets, and simulation outputs. Its ability to accelerate these workflows reduces the time required for analysis, enabling researchers to iterate more rapidly and produce high-quality scientific results.

Enterprise Data Analytics and Cloud Computing

For organizations working with vast amounts of business data, the HPE 876337-001 Tesla V100 accelerates analytics operations and enhances enterprise application performance. The GPU’s parallel architecture supports advanced data mining, real-time analytics, risk modeling, business forecasting, and cloud-accelerated applications. When deployed in HPE servers with multi-GPU support, enterprises can scale analytics workloads efficiently while reducing computational bottlenecks.

Real-Time Analytics Performance

The Tesla V100 accelerates data pipelines that require rapid processing, enabling real-time insights for financial trading, operational monitoring, cybersecurity, and customer behavior analysis. Its ability to process data at high speed allows enterprises to react faster and make informed decisions based on accurate and timely insights.

Cloud GPU Acceleration

With cloud environments increasingly supporting GPU virtualization, the Tesla V100 SXM2 plays a critical role in delivering scalable GPU compute power. HPE servers leveraging V100 accelerators can deploy GPU-accelerated virtual machines for AI, analytics, and HPC workloads, optimizing cloud resource allocation and operational efficiency.

HPE Server Ecosystems

The HPE 876337-001 Tesla V100 SXM2 is fully compatible with selected HPE ProLiant and high-performance computing server models engineered to support SXM2 GPU form factors. Its compatibility ensures efficient integration and optimal power delivery, cooling, and performance management. HPE’s advanced server architecture enhances GPU communication, minimizes downtime, and maximizes total computing output for enterprise and research environments.

Power Integration

HPE servers provide advanced GPU power monitoring, automated fault detection, and intelligent resource allocation. Tesla V100 GPUs benefit from these enhancements to maintain optimal performance and reliability, ensuring stable operation for mission-critical workloads and uninterrupted enterprise service delivery.

Scalable Server Deployment

Organizations deploying multiple Tesla V100 modules within SXM2-enabled servers can scale their GPU resources quickly, supporting workloads that require massive parallel computing. This scalability is essential for environments expanding AI infrastructure, adding new HPC workloads, or modernizing legacy computing systems.

Workload-Specific Use Cases

Scientific Research and Engineering

Commonly used in astrophysics, molecular chemistry, weather prediction, engineering simulations, and large-scale scientific modeling environments where double-precision computation is essential.

Data Analytics and Enterprise Processing

Supports business intelligence pipelines, risk modeling, fraud detection, and real-time analytics for industries requiring fast, accurate data computation.

Cloud Computing

Ideal for GPU-accelerated virtual machines, multi-tenant environments, and cloud platforms hosting AI, HPC, and analytics workloads that need high-throughput GPU resources.

Autonomous Systems and Robotics

Provides the computational power needed for sensor processing, object detection, motion prediction, and autonomous decision-making models used in robotics, drones, and smart vehicle systems.

High-Performance Rendering

Used in medical imaging, design visualization, 3D modeling, and advanced rendering pipelines requiring fast graphics computation and parallel processing capabilities.