876911-001 HPE Nvidia 16GB Tesla HBM2 V100 SXM2 Computational Accelerator

Overview of HPE 876911-001 Nvidia 16GB Tesla HBM2 V100

The HPE 876911-001 Nvidia 16GB Tesla HBM2 V100 SXM2 8-Pin PCIe 3x16 Computational Accelerator represents one of the most powerful GPU-based computation engines engineered for advanced data centers, enterprise HPC infrastructures, AI training clusters, and scientific research workloads demanding exceptional performance. With its integration of Nvidia’s breakthrough Volta architecture, the accelerator is designed to push the limits of AI model development, deep learning inference, large-scale computing, and high-speed data processing.

Volta Architecture and Enhanced Computing Performance

The Nvidia Tesla V100 SXM2 leverages the Volta GPU architecture, a significant leap forward in processing design with improvements to Tensor Core technology, floating-point performance, and parallel computation efficiency. The architecture supports ultra-fast matrix operations, high GPU occupancy, and optimized instruction pipelines, which make it a preferred solution for deep learning frameworks, HPC workloads, and GPU-accelerated application stacks in scientific and enterprise environments. Its optimized design ensures significantly improved computational density, making it possible to execute millions of operations simultaneously with unmatched precision.

Tensor Cores for Accelerated Deep Learning Workloads

Tensor Cores embedded within the Volta architecture enable high-speed mixed-precision computation, which is essential for deep learning model training. These cores drastically reduce the time required to train sophisticated neural network structures such as transformer-based architectures, convolutional neural networks, generative adversarial networks, and recurrent learning systems. The performance improvements enable organizations to train larger models, process richer datasets, and run continuous training cycles without performance degradation.

Mixed Precision

Mixed-precision computing allows developers and researchers to achieve faster results while maintaining high model accuracy. This capability reduces training time for AI models, making it easier to explore hyperparameters, refine learning algorithms, and generate production-ready AI applications. The Tesla V100’s optimized FP16, FP32, and FP64 computation pipelines help in processing diverse workloads ranging from AI-based analytics to simulation-heavy research programs.

CUDA Core Acceleration for Scientific Computation

CUDA cores in the Tesla V100 SXM2 improve large-scale numerical computation, enabling the GPU to execute complex simulations, analytical workloads, and physics-based algorithms. This is especially beneficial for researchers conducting simulations in fluid mechanics, quantum chemistry, seismic analysis, astrophysics, structural analysis, and climate research. The GPU can manage enormous datasets and produce accurate simulation outcomes faster than traditional CPU-only environments.

Double Precision and Single Precision Computing Strength

Double-precision FP64 capabilities enable researchers to solve complex scientific equations with high numerical integrity. Single precision FP32 computing powers a broad range of enterprise workloads requiring rapid parallel processing for imaging algorithms, machine learning pipelines, and engineering modeling. These features ensure that the GPU can serve as a unified accelerator for both precision-focused and performance-focused tasks.

High-Bandwidth 16GB HBM2 Memory Data-Intensive Workloads

The HPE 876911-001 Nvidia Tesla V100 includes 16GB of HBM2 memory, engineered to deliver exceptionally high bandwidth and ultra-fast data movement. This memory design dramatically enhances the GPU’s ability to sustain demanding workloads, particularly in environments that rely on massively parallel computations or large-batch data processing. The memory architecture supports large neural network training sessions, massive simulation models, graph analytics, and real-time data streaming applications that require consistent access to large volumes of data with minimal latency.

Memory Throughput for Multi-GPU Scalability

HBM2 memory provides significantly higher throughput compared to standard memory technologies, enabling multi-GPU clusters to access and transfer data more efficiently. This enhanced memory scaling is essential for deep learning frameworks operating across parallel GPU nodes. Large AI clusters, training farms, and HPC networks experience improved efficiency when multiple Tesla V100 SXM2 modules operate within interconnected server environments powered by HPE infrastructure.

Low Latency for Continuous Computation

Low-latency processing allows models and simulations to operate without computational delays, reducing bottlenecks during iterative cycles, data transformations, and parallel workloads. This capability is critically important for applications where performance consistency directly influences outcome accuracy and operational timelines, including predictive analytics, financial modeling, and autonomous system algorithms.

SXM2 Form Factor for GPU Performance

The SXM2 form factor distinguishes the Tesla V100 from PCIe GPU counterparts by providing superior thermal and electrical characteristics. SXM2 enables higher sustained power delivery, improved heat dissipation, and more efficient GPU utilization, enabling the accelerator to operate at maximum performance over extended periods without throttling. This form factor is specifically designed for demanding data centers where GPU clusters operate under full load continuously.

Improved Thermal Stability and System Cooling

The SXM2 module is engineered to work seamlessly with integrated server cooling systems within HPE computational platforms. This design enables more stable temperature management even during the execution of compute-heavy workloads such as deep learning training, multi-stage simulation modeling, and parallel data analytics. Enhanced cooling efficiency reduces the chance of thermal throttling, ensuring consistent GPU speed and extending hardware life.

High-Bandwidth GPU Interconnect

SXM2 technology facilitates faster GPU-to-GPU communication in multi-GPU server configurations. Improved interconnect speed enhances distributed training efficiency, simulation synchronization, and parallel workload coordination. This advantage is essential for environments that rely on real-time data exchange across multiple accelerators, such as supercomputing clusters, AI development platforms, and computational research centers.

Inference Acceleration

The Tesla V100 SXM2 provides advanced acceleration for machine learning and deep learning workflows, making it a critical component in modern AI computing environments. Its architecture supports a wide variety of neural network types, allowing organizations to train and deploy complex models in significantly shorter time frames. From natural language processing to large-scale computer vision systems, this accelerator improves efficiency for every stage of the AI pipeline.

Enhanced Model Training Speed

The processing capabilities of the V100 reduce training cycles by accelerating matrix operations, gradient calculations, and backpropagation processes. AI developers can conduct rapid experimentation, iterate model designs, and significantly reduce time-to-production. This level of performance is essential for machine learning organizations that work with large datasets and high-dimensional model architectures.

Inference Deployment

Beyond training, the accelerator enhances inference workloads, providing real-time response capabilities for AI-based decision systems. Whether used in automated inspection systems, intelligent robotics, cybersecurity detection engines, or financial prediction models, the V100 delivers rapid inference performance with high accuracy.

Industry-Specific

The V100 supports crucial AI applications across industries including automotive autonomous driving systems, healthcare diagnostics powered by imaging AI, financial market modeling and fraud detection, industrial automation, and smart surveillance systems. Its extensive computational range enables organizations to deploy advanced AI programs securely and reliably.

High-Performance Computing for Scientific Workloads

The HPE 876911-001 Tesla V100 is designed to handle the extreme demands of HPC workloads. Its architecture supports advanced computations essential for simulation-driven research in academic, scientific, and engineering fields. Researchers rely on its precision and speed to complete complex workloads in less time, increasing productivity and enabling deeper exploration in various scientific domains.

Scientific Modeling and Simulation

The accelerator supports multi-physics simulations, astrophysical modeling, finite element analysis, and environmental research. These simulations benefit from the V100’s high floating-point accuracy and parallel processing capabilities, allowing scientists to explore scenarios, identify patterns, and conduct experiments virtually with improved reliability.

Molecular Modeling and Chemical Research

Advanced GPU computing is essential in molecular dynamics, protein structure prediction, and chemical reaction simulations. The V100 supports these tasks with its ability to process molecular interactions, analyze atomic-scale movements, and accelerate calculations necessary for drug development, genetic research, and chemical engineering.

Enterprise Data Analytics and Computational Intelligence

Businesses increasingly rely on large-scale analytics to power decisions, forecast trends, and manage massive datasets. The Tesla V100 SXM2 enhances data throughput, enabling enterprises to unlock insights faster and develop data-driven strategies that support growth. Its architecture supports advanced analytics frameworks, enabling real-time processing, predictive modeling, and AI-enabled business intelligence.

Real-Time Big Data Processing

The GPU processes complex datasets quickly, making it ideal for streaming analytics, risk analysis, identity verification, supply chain optimization, and other enterprise-critical analytical tasks. The ability to compute large datasets in real time provides organizations with a competitive edge in fast-moving industries.

Cloud Acceleration and GPU Deployments

Cloud computing environments require high-performance GPU instances for AI-as-a-service, HPC-as-a-service, and analytics-as-a-service offerings. The V100 supports virtual GPU configurations that allow multiple users or workloads to share GPU resources efficiently, enabling scalable performance across diverse cloud applications.

Integration with HPE Server Platforms

The HPE 876911-001 GPU is engineered for seamless integration with HPE servers supporting SXM2 GPU configurations. These platforms offer optimized cooling, power delivery, and system architecture enhancements, ensuring stable and reliable accelerator performance for enterprise and research applications.

Advanced Power and Performance

HPE server systems provide intelligent monitoring tools that help administrators track GPU performance, manage workload distribution, and maintain energy efficiency. This integration ensures consistent computational stability even under heavy workloads.

Scalable Multi-GPU Cluster Configurations

Enterprises and research institutions can scale their AI or HPC performance by deploying multiple Tesla V100 SXM2 accelerators in parallel within HPE servers. This scalability supports advanced research clusters, supercomputing nodes, deep learning farms, and enterprise analytics centers requiring rapid expansion and continuous workload support.