699-2H400-0201-510 Nvidia16GB PCI Express Graphics Card.

Nvidia 699-2H400-0201-510 16GB Tesla P100 Graphics Card

Designed for data center deployment, the NVIDIA 699-2H400-0201-510 Tesla P100 is a powerful GPGPU accelerator. Leveraging Pascal architecture and 16GB of high-bandwidth HBM2 memory, it delivers exceptional performance for high-performance computing (HPC), deep learning, and complex simulation workloads.

General Information

• Brand: Nvidia
• Manufacturer Part Number: 699-2H400-0201-510
• Product Series: Tesla Data Center Accelerator
• GPU Architecture: Nvidia Pascal
• Processor Model: Tesla P100

Technical Specifications

• CUDA Core Count: 3,584 parallel processing cores
• GPU Base Clock Frequency: 1,190 MHz
• Double-Precision (FP64) Compute: 4.7 TFLOPS
• Single-Precision (FP32) Compute: 9.3 TFLOPS
• Total Memory Capacity: 16GB High-Bandwidth Memory 2 (HBM2)
• Advanced Packaging: Chip-on-Wafer-on-Substrate (CoWoS) integration
• Memory Interface Width: Massive 4096-bit bus
• Memory Data Rate: 715 MHz (effective rate is higher)
• Peak Memory Throughput: 732 GB/s bandwidth

System Integration & Power Specifications

Host Interface & Power Draw

• Expansion Bus: PCI Express 3.0 x16 interface
• Maximum Power Consumption: 250 Watts
• Form Factor: Standard full-height, dual-slot accelerator card
• Cooling Solution: Active fan cooling for sustained data center operation

Software & API Support

• Graphics API Support: DirectX 12.1, OpenGL 4.6
• Compute Frameworks: Full support for CUDA, OpenCL, and other parallel computing platforms
• Target Drivers: Enterprise-grade NVIDIA data center drivers

Nvidia 699-2H400-0201-510 16GB Graphics Card Overview

The Nvidia 699-2H400-0201-510 16GB PCI Express Tesla P100 4096 Bit HBM2 X16 Accelerator Graphics Card belongs to a high-end category of enterprise-grade GPU accelerators designed specifically for data center, scientific, and professional computing environments. This category focuses on massively parallel processing, extreme memory bandwidth, deterministic performance, and long-term operational stability rather than graphical output or consumer visualization tasks. Products in this category are engineered to function as core computational engines within servers, accelerating workloads that demand immense floating-point performance and sustained throughput.Within modern IT infrastructures, this accelerator category plays a pivotal role in enabling organizations to transition from traditional CPU-centric architectures to heterogeneous computing models. By combining CPUs for control and orchestration with GPUs for parallel computation, enterprises can achieve dramatic improvements in performance efficiency, scalability, and energy utilization. The Tesla P100 class exemplifies this category by delivering consistent compute acceleration across a wide range of demanding workloads.

Nvidia Tesla P100 Accelerator Subcategory Positioning

The Tesla P100 accelerator belongs to a specialized subcategory within Nvidia’s data center GPU portfolio that emphasizes compute density, memory bandwidth, and architectural efficiency. This subcategory was designed to address the growing demand for accelerated computing in artificial intelligence, machine learning, deep learning training, and high performance computing. Unlike general-purpose graphics cards, Tesla accelerators are optimized for headless operation and are intended to be deployed in rack-mounted servers and clustered environments.

This subcategory is characterized by its support for enterprise software ecosystems, long lifecycle availability, and rigorous validation processes. The Nvidia 699-2H400-0201-510 model represents a refined implementation of the Tesla P100 design, offering consistent performance and compatibility across diverse server platforms. This positioning makes it suitable for organizations that require predictable results and long-term infrastructure planning.

Accelerator-First Design and Compute Orientation

The design approach of this subcategory is centered on maximizing compute throughput per unit of power and space. Accelerator-first architecture ensures that silicon resources are dedicated to numerical computation rather than graphics rendering or display processing. This orientation allows the Tesla P100 category to deliver superior performance in floating-point operations, matrix calculations, and parallel algorithms.

By focusing on compute efficiency, this subcategory enables enterprises to consolidate workloads onto fewer systems while achieving higher overall performance. This consolidation reduces hardware footprint, simplifies management, and lowers total cost of ownership across data center deployments.

Headless Deployment and Server Integration

Headless deployment is a defining feature of this accelerator category, enabling seamless integration into enterprise servers without reliance on display interfaces. This simplifies system design and allows GPUs to be deployed densely within server chassis. Headless operation also aligns with automated provisioning, monitoring, and orchestration tools commonly used in large-scale data centers.

Server integration is further enhanced by standardized form factors, firmware compatibility, and driver support. This ensures that the accelerator can be deployed across a wide range of OEM platforms without extensive customization.

HBM2 Memory Technology and 4096 Bit Interface

The use of High Bandwidth Memory 2 is a defining attribute of the Tesla P100 accelerator category. HBM2 memory is designed to deliver exceptional bandwidth while maintaining lower power consumption compared to traditional memory technologies. The 4096 bit memory interface provides an expansive data path that enables rapid movement of data between memory and compute cores.

In this category, the 16GB HBM2 memory capacity is optimized for workloads that require both large memory footprints and high-speed access. This configuration supports complex neural networks, large simulation datasets, and advanced analytics workloads that benefit from keeping data resident on the GPU.

High Bandwidth Advantages for Parallel Processing

Parallel processing workloads rely heavily on memory bandwidth to keep compute units fully utilized. The Tesla P100 category excels in this regard by providing sustained high-bandwidth memory access that minimizes stalls and bottlenecks. This advantage is particularly important for deep learning training, where large tensors and matrices are processed repeatedly.

By delivering consistent memory throughput, this category ensures that computational resources operate at peak efficiency, maximizing performance gains from GPU acceleration.

Latency Reduction and Energy Efficiency

HBM2 technology reduces latency by placing memory stacks in close proximity to the GPU die. This architectural approach shortens signal paths and improves data access times. Lower latency translates into faster execution of memory-bound operations and improved overall application responsiveness.

Energy efficiency is another key benefit of HBM2 memory. Reduced power consumption per bit of data transferred supports data center efficiency goals and enables higher compute density within thermal and power constraints.

PCI Express X16 Connectivity and System Throughput

The PCI Express X16 interface defines the connectivity standard for this accelerator category, enabling high-speed communication between the GPU and the host system. This interface supports rapid data transfers that are essential for workloads involving frequent interaction between CPUs and GPUs. In enterprise environments, efficient interconnects are critical for maintaining balanced system performance.

Standardized PCI Express connectivity ensures broad compatibility with server platforms, simplifying deployment and scaling. This allows organizations to integrate Tesla P100 accelerators into existing infrastructures without extensive redesign.

Multi-GPU Configuration and Horizontal Scaling

This category is designed to support multi-GPU configurations, enabling organizations to scale compute capacity by adding additional accelerators. Multi-GPU systems allow workloads to be distributed across GPUs, reducing execution time and improving throughput. This scalability is essential for deep learning training and large-scale simulations.

Horizontal scaling across clusters further enhances capacity, allowing data centers to build powerful GPU-accelerated environments that grow alongside workload demands.

Efficient CPU and GPU Collaboration

Effective collaboration between CPUs and GPUs is a cornerstone of heterogeneous computing. This category supports balanced architectures where CPUs manage control flow and GPUs execute parallel computations. High-bandwidth PCI Express connectivity ensures that data movement between processors remains efficient.

This collaboration model improves overall system utilization and enables applications to 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 this accelerator category. The Tesla P100 was engineered to deliver high performance for both training and inference tasks, making it suitable for a wide range of AI applications. Its architecture supports efficient execution of neural network operations, including convolution, matrix multiplication, and activation functions.

By accelerating AI workloads, this category enables organizations to process larger datasets, train more complex models, and deploy intelligent applications more rapidly. This capability is critical for enterprises seeking to gain insights and automation advantages through AI.

Deep Learning Training Efficiency

Deep learning training involves extensive numerical computation and iterative optimization. GPUs in this category excel at these tasks by executing thousands of operations in parallel. The combination of high compute throughput and HBM2 memory bandwidth significantly reduces training times compared to CPU-only systems.

Support for multi-GPU training further enhances scalability, allowing organizations to distribute workloads across accelerators and achieve faster convergence.

Inference Performance and Predictable Latency

Inference workloads require low latency and consistent performance, particularly in production environments. This category provides deterministic execution characteristics that ensure reliable response times. This reliability is essential for applications such as real-time analytics, recommendation engines, and autonomous systems.

The ability to use the same accelerator platform for both training and inference simplifies infrastructure management and reduces operational complexity.

High Performance Computing and Scientific Simulation

High performance computing represents a foundational use case for this accelerator category. Scientific simulations, engineering analysis, and computational research benefit from the parallel processing capabilities of the Tesla P100. By accelerating numerical calculations, this category enables researchers to tackle complex problems more efficiently.

Support for double-precision and mixed-precision computation ensures accuracy and flexibility across a range of scientific applications. This makes the category suitable for workloads that demand both performance and numerical fidelity.

Simulation, Modeling, and Analysis Workloads

Simulation and modeling workloads often involve large datasets and repetitive calculations. GPUs in this category accelerate these processes by distributing computations across thousands of cores. This parallelism significantly reduces execution times and enables more detailed simulations.

Engineering and scientific teams can leverage this capability to explore more scenarios, refine models, and achieve deeper insights.

Academic and Research Infrastructure Deployment

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

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

Enterprise Virtualization and Cloud Computing

This accelerator category is also well-suited for virtualization and cloud computing environments. By enabling GPU acceleration within virtual machines and containers, organizations can deliver high-performance services to multiple users. This capability supports a wide range of enterprise workloads, including data analytics, AI services, and accelerated applications.

Cloud service providers leverage this category to offer GPU-accelerated instances with predictable performance and isolation. Enterprises benefit from the flexibility to allocate GPU resources dynamically based on workload requirements.

GPU Sharing and Resource Optimization

Virtualized environments benefit from GPU sharing by improving resource utilization. This category supports integration with virtualization platforms, enabling efficient allocation of GPU resources while maintaining performance isolation. This optimizes infrastructure usage and reduces costs.

By supporting GPU acceleration in virtualized settings, this category enables organizations to consolidate workloads and enhance operational efficiency.

Reliability and Continuous Operation Standards

Enterprise environments demand hardware that can operate continuously under sustained workloads. This category is engineered with enterprise-grade components and undergoes rigorous validation to ensure long-term reliability. Continuous operation capabilities minimize downtime and support mission-critical applications.

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