699-2G500-0202-460 Nvidia 32GB HBM2 Tesla V100 PCIE GPU Accelerator Card

Nvidia 699-2G500-0202-460 32GB HBM2 Tesla V100 PCIE GPU Accelerator Card Overview

The Nvidia 699-2G500-0202-460 32GB HBM2 Tesla V100 PCIE GPU Accelerator Card represents one of the most advanced computational GPU accelerators designed for artificial intelligence, deep learning, scientific research, and high-performance data analytics. Engineered with the groundbreaking Volta architecture, this accelerator card delivers exceptional compute performance, energy efficiency, and data throughput. It supports massive parallel processing capabilities that cater to data centers, AI researchers, and developers looking to achieve superior acceleration for complex workloads and advanced simulations.

Architecture and Design Excellence

The Tesla V100 accelerator card is built on Nvidia’s Volta GPU architecture, a platform specifically designed for data-intensive and high-computing tasks. This architecture integrates advanced CUDA cores and Tensor cores that drastically enhance the speed of deep learning training and inferencing. With over 5,000 CUDA cores, the Nvidia 699-2G500-0202-460 card provides robust parallelism, enabling researchers to execute multiple operations simultaneously. Its hardware structure promotes optimal heat dissipation and stability, ensuring consistent performance under heavy workloads and extended operation cycles.

Volta GPU Architecture Innovations

The Volta architecture introduces an innovative design that focuses on deep learning and artificial intelligence computations. This GPU integrates specialized Tensor Cores that deliver up to 125 teraflops of deep learning performance, making it a leading solution for accelerating neural networks. The integration of high-speed NVLink and PCIe interface ensures that data transfer between the CPU and GPU is seamless and efficient. The Tesla V100 also supports mixed-precision computing, balancing precision and performance for a wide variety of professional workloads.

Enhanced CUDA and Tensor Cores

CUDA and Tensor Cores play a pivotal role in boosting parallel processing performance. CUDA cores handle floating-point and integer computations for traditional GPU workloads, while Tensor Cores optimize deep learning matrix operations. This combination allows the Nvidia 699-2G500-0202-460 to deliver exponential speed improvements compared to previous GPU generations. These cores enable faster model training, real-time inferencing, and efficient multi-GPU scaling in data-intensive environments.

Memory Configuration and Data Bandwidth

The Tesla V100 32GB HBM2 accelerator card comes with a massive 32GB of second-generation High Bandwidth Memory (HBM2). This memory configuration delivers an extraordinary memory bandwidth of up to 900 GB/s, which ensures that data is readily accessible for GPU computations. The high bandwidth memory enables faster access to large datasets, essential for artificial intelligence, scientific modeling, and high-performance computing (HPC) workloads. The HBM2 memory technology supports error correction, stability, and consistent throughput, making it suitable for enterprise and research applications.

HBM2 Memory Performance

HBM2 memory stands out for its stacked structure that allows for shorter data paths and lower latency. Compared to traditional GDDR memory, HBM2 reduces power consumption while improving bandwidth efficiency. The Tesla V100 leverages this advantage to achieve seamless performance in multi-layer neural networks and simulation workloads. Its 32GB memory capacity allows users to manage massive datasets without memory bottlenecks, ensuring high computational throughput across varying workloads.

Energy Efficiency and Power Management

The Nvidia 699-2G500-0202-460 GPU Accelerator Card features an optimized power envelope designed for energy efficiency without compromising computational speed. It typically consumes around 250W, which is distributed intelligently across processing units and memory modules. Nvidia’s GPU Boost technology dynamically adjusts clock speeds based on thermal and power conditions, optimizing energy usage while maintaining stable performance levels. This intelligent power management contributes to lower operational costs in large-scale deployments.

Thermal Design and Reliability

Designed for data center environments, the Tesla V100 maintains robust thermal stability through its advanced cooling mechanisms. The card supports passive cooling, relying on system-level airflow to dissipate heat efficiently. This design ensures durability, reduces maintenance requirements, and supports 24/7 continuous operation under demanding workloads. Its reliable thermal management system helps prevent performance throttling and extends the operational lifespan of the GPU.

Performance Capabilities and Computational Power

The Nvidia 699-2G500-0202-460 Tesla V100 GPU Accelerator delivers exceptional computational performance, with up to 7.8 TFLOPS of double-precision (FP64) performance and over 15.7 TFLOPS of single-precision (FP32) computing power. These specifications make it ideal for handling complex simulations, machine learning models, and scientific computations. In mixed-precision mode, the V100 achieves superior throughput that significantly reduces training times for large-scale AI models.

AI and Deep Learning Workloads

The Tesla V100 is purpose-built for AI and deep learning acceleration. It accelerates frameworks such as TensorFlow, PyTorch, Caffe, and MXNet, allowing developers to achieve higher throughput in both training and inferencing stages. The inclusion of Tensor Cores enables faster matrix multiplications, which are the backbone of neural network computations. This GPU enhances model precision and scalability across multi-node configurations, supporting advanced AI research and production-level deployment.

Scientific and Research Applications

Beyond AI, the Nvidia 699-2G500-0202-460 Tesla V100 card is widely adopted in scientific computing, molecular dynamics, seismic analysis, fluid dynamics, and computational chemistry. Its ability to handle double-precision workloads makes it suitable for simulations that require high numerical accuracy. Research institutions and laboratories rely on its robust performance to conduct large-scale data analysis, accelerate discovery processes, and model complex physical systems with precision and reliability.

High-Performance Computing (HPC) Integration

The Tesla V100 integrates seamlessly into high-performance computing clusters, offering support for multi-GPU scaling through NVLink technology. This interconnect allows for ultra-fast data exchange between GPUs, resulting in higher performance efficiency in distributed computing environments. It reduces communication bottlenecks, improving scalability and throughput in parallel processing systems. HPC centers utilize this card for tasks such as weather prediction, astrophysics simulations, and computational fluid dynamics (CFD).

Compatibility and System Integration

The Nvidia 699-2G500-0202-460 32GB HBM2 Tesla V100 PCIE GPU Accelerator Card is designed for broad compatibility with various server and workstation configurations. It uses a PCI Express 3.0 x16 interface, ensuring smooth integration into most enterprise-grade computing systems. The card supports major operating systems, including Linux and Windows Server environments, and is compatible with major GPU computing platforms and APIs such as CUDA, OpenCL, and OpenACC.

Data Center Deployment

This GPU accelerator is optimized for deployment in data centers that require high-density computing. Its PCIe form factor enables flexibility in multi-GPU configurations, allowing organizations to scale their infrastructure according to computational demands. The Tesla V100 supports virtualization technologies, enabling resource sharing and improved workload distribution across multiple virtual machines. This flexibility makes it an excellent choice for cloud computing and AI-as-a-Service environments.

Driver and Firmware Optimization

The Nvidia 699-2G500-0202-460 card receives continuous driver updates that ensure compatibility with evolving operating systems and software frameworks. Nvidia’s enterprise-level support provides robust stability and optimized firmware designed for mission-critical operations. These updates enhance performance, security, and reliability, ensuring that the card remains fully functional and efficient throughout its lifecycle.

Scalability and Multi-GPU Performance

The Tesla V100 supports Nvidia NVLink, a high-speed interconnect technology that allows multiple GPUs to function as a unified computational unit. This connectivity increases total available memory, enabling efficient multi-GPU scaling for larger datasets and models. The NVLink architecture offers up to 300 GB/s of GPU-to-GPU bandwidth, which eliminates communication barriers found in traditional PCIe configurations. It is ideal for deep learning clusters and distributed HPC applications where seamless data sharing is essential.

Parallel Processing Advantages

Parallelism lies at the core of the Tesla V100’s architecture. With thousands of CUDA cores and multiple Tensor Cores, this GPU can process numerous tasks concurrently, minimizing latency and maximizing throughput. In machine learning applications, it speeds up both data preprocessing and model computation, allowing researchers to iterate and test new algorithms faster. Parallelism also benefits simulation-based applications that rely on simultaneous computation across multiple data nodes.

NVLink and PCIe Integration

The dual-interface capability of NVLink and PCIe enhances communication flexibility between GPUs and CPUs. PCIe 3.0 ensures compatibility with existing server infrastructure, while NVLink provides the next level of interconnect speed and reliability. This combination makes the Tesla V100 suitable for both traditional and advanced computing setups. It ensures a balance between backward compatibility and forward-looking scalability for next-generation workloads.

Cluster-Level Performance Optimization

In cluster computing environments, the Tesla V100 offers dynamic scalability through Nvidia’s NVSwitch and DGX system architecture. These solutions enable multiple GPUs to communicate over high-bandwidth pathways, maintaining consistent performance across nodes. The architecture supports dynamic workload balancing, ensuring optimal utilization of all GPUs within a cluster. This design improves the efficiency of AI training clusters and scientific computing farms that demand continuous, high-performance output.

Artificial Intelligence and Machine Learning

In AI applications, the Tesla V100 is instrumental in accelerating deep neural network training and inferencing. It shortens the development cycle for AI models by processing massive datasets rapidly, which enhances prediction accuracy and response time. Companies implementing AI-driven automation, natural language processing, and computer vision benefit from the card’s immense computational speed and memory bandwidth.

Data Science and Analytics

Data scientists leverage the Tesla V100 for analytics, modeling, and visualization tasks that require handling terabytes of data. Its superior memory throughput allows faster data ingestion and model computation, leading to quicker insights and decision-making. The GPU’s compatibility with major data frameworks, including RAPIDS and Apache Spark, further strengthens its utility in data-intensive enterprises.

Scientific Research and Simulation

Universities and research laboratories utilize the Tesla V100 for physics simulations, genomics sequencing, and chemical modeling. Its high double-precision performance supports the mathematical accuracy required in scientific workloads. The GPU’s ability to run parallel simulations accelerates discovery timelines and enhances research productivity, especially in climate modeling, material sciences, and bioinformatics.

Advanced Features and Technologies

The Nvidia Tesla V100 integrates a range of advanced technologies that differentiate it from previous GPU generations. These include Tensor Core acceleration, NVLink interconnect, mixed-precision computing, and AI-optimized performance enhancements. Each of these technologies contributes to faster computation, reduced latency, and improved overall efficiency.

Mixed-Precision Computing

Mixed-precision computing enables the Tesla V100 to balance performance and accuracy by combining FP16 and FP32 data formats. This technique enhances deep learning efficiency, allowing the GPU to process more operations per second without sacrificing accuracy. Mixed-precision training is particularly beneficial for large-scale AI models, enabling researchers to train networks faster and with lower energy consumption.

Reliability and Operational Efficiency

The Nvidia 699-2G500-0202-460 card is engineered for enterprise-grade reliability, ensuring long-term stability under continuous workloads. It undergoes rigorous testing and validation processes to guarantee operational integrity in mission-critical applications. Its build quality and firmware optimizations ensure dependable performance, reducing downtime and maintenance requirements in production environments.

Data Center Durability

Data centers require components that can perform consistently under heavy load, and the Tesla V100 delivers on this requirement. Its passive cooling design, power regulation, and thermal efficiency make it suitable for 24/7 operations. The card is also designed for compatibility with Nvidia’s GPU management tools, which enable administrators to monitor temperature, utilization, and performance metrics in real-time.

Firmware and Hardware Stability

Advanced firmware ensures the card remains stable even under fluctuating workloads. Nvidia’s consistent driver updates provide compatibility with evolving data center software stacks, ensuring seamless integration into HPC systems. Hardware-level ECC protection further enhances data integrity, reducing computational errors and ensuring accuracy in mission-critical applications.