699-2H400-0201-530 Nvidia 16GB PCI Express Graphics Card.

Nvidia 699-2H400-0201-530 16GB PCI Express Graphics Card

Optimized for enterprise computational tasks, the NVIDIA 699-2H400-0201-530 Tesla P100 PCIe accelerator delivers high-performance parallel processing. With 16GB of CoWoS HBM2 memory on a 4096-bit bus, this computing card excels in deep learning, scientific simulation, and high-performance computing workloads.

General Information

• Manufacturer: NVIDIA Corporation
• Part Identifier: 699-2H400-0201-530
• Product Type: Tesla Data Center GPU Accelerators

Technical Specifications

• Streaming Processor Count: 3,584 CUDA parallel computing cores
• Graphics Processor Base Clock: 1,190 MHz
• Processing Architecture: Pascal GPU Architecture
• Compute Processor: Tesla P100
• Card Form Factor: Full-height, dual-slot PCIe card
• Double-Precision Compute Rate: 4.7 teraflops (FP64)
• Single-Precision Compute Rate: 9.3 teraflops (FP32)
• Dedicated Memory Capacity: 16GB High Bandwidth Memory 2
• Memory Packaging Technology: Chip-on-Wafer-on-Substrate (CoWoS) design
• Memory Bus Configuration: 4096-bit wide interface
• Memory Clock Frequency: 715 MHz
• Maximum Memory Throughput: 732 gigabytes per second bandwidth

System Integration & Interface Specifications

Platform Connectivity & Power

• Host Connection Interface: PCI Express 3.0 x16 bus
• Maximum Thermal Design Power: 250 watts
• Thermal Management: Active cooling solution with integrated fan
• Power Connectors: Typically requires auxiliary PCIe power connections

Software Ecosystem & API Compatibility

• Graphics API Standards: DirectX 12.1, OpenGL 4.6 compatibility
• Parallel Computing Frameworks: Full CUDA platform support, OpenCL compatibility
• Operating System Support: Enterprise Linux distributions, Windows Server editions
• Development Environment: Compatible with major AI and HPC software stacks

Nvidia 699-2H400-0201-530 16GB Graphics Card Overview

The Nvidia 699-2H400-0201-530 16GB PCI Express Tesla P100 4096 Bit HBM2 X16 Accelerator Graphics Card is positioned within a highly specialized data center GPU accelerator category focused on accelerating compute-heavy workloads across enterprise, scientific, and cloud infrastructures. This category is not oriented toward visual graphics rendering but instead emphasizes numerical computation, parallel processing efficiency, and sustained performance under continuous operational demands. GPU accelerators in this category are engineered to act as dedicated processing units that complement CPUs by handling workloads that benefit from massive parallelism.As organizations increasingly adopt data-driven and AI-powered strategies, the importance of accelerator-centric architectures continues to grow. This category supports that transition by enabling heterogeneous computing environments where GPUs execute data-parallel tasks while CPUs manage orchestration and control. The Tesla P100 accelerator class reflects a mature stage in this evolution, offering a balance of compute density, memory bandwidth, and enterprise-grade reliability suitable for long-term deployment.

Tesla P100 Subcategory Within Nvidia Accelerator Portfolio

The Tesla P100 belongs to a distinct subcategory within Nvidia’s data center accelerator lineup that was designed to address the rising demands of artificial intelligence, machine learning, and high performance computing workloads. This subcategory is defined by its focus on floating-point throughput, memory efficiency, and predictable performance characteristics rather than consumer graphics features. Tesla accelerators are validated specifically for server and cluster environments, ensuring stable operation in mission-critical systems.The Nvidia 699-2H400-0201-530 variant represents a refined implementation within this subcategory, maintaining consistency in architecture and compatibility while offering deployment flexibility across a wide range of enterprise platforms. Organizations adopting this subcategory benefit from long lifecycle availability, standardized software support, and integration with widely used compute frameworks.

Compute-Optimized Architecture and Design Intent

The Tesla P100 accelerator subcategory is built with a compute-optimized architecture that allocates the majority of silicon resources to numerical processing units. This design intent enables high throughput for workloads involving linear algebra, vector operations, and large-scale mathematical modeling. By removing unnecessary graphics-related components, the accelerator achieves higher efficiency and reliability for compute-centric use cases.

This architectural focus allows enterprises to deploy fewer accelerators while achieving higher overall performance, reducing infrastructure complexity and operational overhead. The result is a scalable platform capable of supporting evolving computational requirements.

Headless Configuration for Dense Server Deployment

Headless configuration is a defining characteristic of this accelerator category, enabling deployment without display outputs or user-facing interfaces. This approach simplifies server design and allows GPUs to be installed in high-density configurations within rack-mounted systems. Headless operation also aligns with automated management tools commonly used in modern data centers.

By supporting dense deployment models, this category enables organizations to maximize compute capacity per rack while maintaining consistent airflow, power distribution, and thermal characteristics.

High Bandwidth Memory 2 Architecture and 4096 Bit Data Path

High Bandwidth Memory 2 technology is central to the performance profile of the Tesla P100 accelerator category. HBM2 provides substantially higher memory bandwidth compared to traditional memory technologies, making it particularly well-suited for data-intensive workloads. The 4096 bit memory interface creates a wide data path that enables rapid movement of information between memory and compute cores.

The 16GB HBM2 memory capacity associated with this category is optimized to accommodate large datasets and complex models directly on the GPU. Keeping data resident in high-speed memory reduces latency and minimizes reliance on slower system memory transfers, resulting in improved efficiency for memory-bound applications.

Bandwidth-Driven Efficiency for Parallel Algorithms

Parallel algorithms rely heavily on memory bandwidth to sustain high levels of performance. The Tesla P100 category excels in this area by delivering consistent and predictable memory throughput. This capability ensures that compute cores remain active and productive, avoiding stalls caused by data access delays.

Applications such as deep learning training, large-scale simulations, and data analytics benefit significantly from this bandwidth-driven efficiency. Faster data access translates directly into reduced execution times and improved throughput.

Latency Reduction and Thermal Efficiency Advantages

The physical integration of HBM2 memory stacks close to the GPU die reduces signal travel distances, resulting in lower memory access latency. Reduced latency improves responsiveness for workloads that frequently access memory, enhancing overall application performance.

HBM2 also contributes to thermal and power efficiency by lowering energy consumption per bit transferred. This efficiency supports higher compute density within data centers and helps organizations manage power and cooling constraints more effectively.

PCI Express X16 Interface and Platform Compatibility

The PCI Express X16 interface serves as the primary communication channel between the Tesla P100 accelerator and the host system. This high-speed interconnect supports rapid data transfers, which are essential for workloads that require frequent synchronization between CPUs and GPUs. Efficient host connectivity is critical for maintaining balanced performance in heterogeneous computing architectures.

Standardized PCI Express connectivity ensures broad compatibility with enterprise server platforms from leading manufacturers. This allows organizations to integrate Tesla P100 accelerators into existing infrastructures with minimal disruption, supporting incremental scaling and flexible deployment strategies.

Scalable Multi-GPU Configurations

Scalability is a core strength of the Tesla P100 accelerator category. Multiple GPUs can be deployed within a single server to increase compute capacity, or distributed across clusters to support large-scale workloads. Multi-GPU configurations enable parallel execution of tasks, reducing processing times and increasing overall throughput.

This scalability is particularly valuable for deep learning training and high performance computing applications that benefit from distributed processing across multiple accelerators.

Balanced CPU and GPU Workload Distribution

Effective workload distribution between CPUs and GPUs is essential for optimizing system performance. In this category, CPUs handle sequential processing and system control, while GPUs execute highly parallel computations. High-bandwidth PCI Express connectivity ensures that data movement between processors remains efficient.

This balanced distribution model maximizes resource utilization and enables applications to fully leverage the strengths of both processing architectures.

Artificial Intelligence and Machine Learning Acceleration

Artificial intelligence and machine learning workloads are primary drivers for the adoption of the Tesla P100 accelerator category. The architecture is optimized to support the computational patterns common in neural network training and inference, including matrix multiplication and tensor operations. This makes the category well-suited for applications such as image recognition, natural language processing, and predictive analytics.

By accelerating AI workloads, this category enables organizations to process larger datasets, experiment with more complex models, and deploy intelligent systems more rapidly. These capabilities are essential for enterprises seeking to derive value from data-driven insights.

Training Performance and Development Efficiency

Deep learning training requires extensive computational resources and efficient memory access. GPUs in this category excel at executing training workloads by parallelizing operations across thousands of compute cores. High memory bandwidth ensures that training processes are not constrained by data access limitations.

Support for distributed training across multiple GPUs further enhances development efficiency, enabling faster model iteration and reduced time to deployment.

Inference Consistency and Production Deployment

Inference workloads demand consistent performance and predictable latency, particularly in production environments. The Tesla P100 category delivers deterministic execution characteristics that ensure reliable response times. This reliability is critical for real-time applications and enterprise services.

Using a single accelerator platform for both training and inference simplifies infrastructure management and reduces operational complexity.

High Performance Computing and Scientific Workloads

High performance computing remains a foundational use case for the Tesla P100 accelerator category. Scientific research, engineering simulations, and numerical modeling benefit from the massive parallel processing capabilities of this GPU. By accelerating complex calculations, the category enables researchers to solve problems more efficiently and explore more detailed scenarios.

Support for double-precision and mixed-precision computation allows applications to balance accuracy and performance, making the category suitable for a wide range of scientific disciplines.

Simulation, Modeling, and Computational Analysis

Simulation and modeling workloads often involve iterative calculations over large datasets. GPUs in this category accelerate these processes by distributing computations across thousands of cores. This parallelism reduces execution times and enables more comprehensive analysis.

Engineers and scientists can leverage this capability to improve design accuracy, optimize systems, and gain deeper insights from computational models.

Research and Academic Computing Environments

Academic institutions and research organizations deploy this accelerator category to support shared computing clusters and supercomputing facilities. The scalability and reliability of Tesla P100 accelerators make them suitable for multi-user environments where consistent performance is essential.

Long-term software support and stable driver ecosystems ensure that research projects remain reproducible and maintainable over extended periods.

Enterprise Virtualization and Cloud Infrastructure

The Tesla P100 accelerator category is highly relevant for virtualization and cloud computing environments. By enabling GPU acceleration within virtual machines and containers, organizations can deliver high-performance compute services to multiple users and applications. This capability supports a wide range of enterprise workloads, including analytics, AI services, and compute-intensive applications.

Cloud service providers utilize this category to offer GPU-accelerated instances with predictable performance and isolation. Enterprises benefit from flexible resource allocation and the ability to scale GPU resources based on demand.

Virtualized GPU Acceleration and Resource Optimization

Virtualized environments benefit from GPU acceleration by offloading compute-intensive tasks from CPUs. This category supports integration with virtualization platforms, enabling efficient sharing of GPU resources while maintaining performance isolation. This improves overall resource utilization and reduces infrastructure costs.

By supporting GPU acceleration in virtualized settings, the Tesla P100 category enables organizations to consolidate workloads and optimize data center efficiency.

Reliability and Continuous Operation Standards

Enterprise and cloud deployments require hardware capable of continuous operation under sustained workloads. The Tesla P100 accelerator category is engineered with enterprise-grade components and undergoes rigorous validation to ensure long-term reliability. Continuous operation minimizes downtime and supports mission-critical services.

Stable firmware and driver support further enhance operational stability, ensuring consistent performance throughout the product lifecycle.

Lifecycle Management and Long-Term Platform Stability

Lifecycle management is a critical factor in enterprise infrastructure planning, and this category benefits from extended product availability and long-term support commitments. Organizations can standardize on the Tesla P100 accelerator platform and plan upgrades strategically. Compatibility with evolving software frameworks ensures ongoing relevance as computing demands continue to evolve.The Nvidia 699-2H400-0201-530 16GB PCI Express Tesla P100 4096 Bit HBM2 X16 Accelerator Graphics Card exemplifies the strengths of this category by delivering high compute density, exceptional memory bandwidth, scalability, and enterprise-grade reliability for data center, scientific, and professional computing environments.