VFJ45 Dell Nvidia Tesla V100 16GB HBM2 PCIE 3.0 X16 250W Graphics Card

The Dell VFJ45 Nvidia Tesla V100 16GB HBM2 PCIe 3.0 GPU

The Dell VFJ45 Nvidia Tesla V100 16GB HBM2 PCIe 3.0 x16 250W Passive CUDA GPU Accelerator Card represents one of the most powerful and efficient data center GPU solutions for demanding workloads. This high-performance accelerator is designed for machine learning, deep learning, artificial intelligence (AI), and high-performance computing (HPC) environments. Built on the NVIDIA Volta architecture, the Tesla V100 offers exceptional computational power, improved memory bandwidth, and optimized performance per watt for enterprises and research institutions.

The Dell VFJ45 variant of the Nvidia Tesla V100 is specifically engineered for compatibility with Dell PowerEdge servers and high-density computing systems. Its passive cooling design and PCIe 3.0 x16 interface make it an ideal choice for data center scalability and parallel computing operations. This accelerator allows businesses and researchers to harness GPU-based acceleration for tasks that previously required large CPU clusters, significantly reducing operational costs and energy consumption

Performance Architecture

The Tesla V100 GPU is based on the NVIDIA Volta architecture, featuring Tensor Cores that enable mixed-precision computing. These Tensor Cores accelerate deep learning training and inference tasks, providing up to 120 teraflops of deep learning performance. This makes it ideal for AI workloads such as image recognition, natural language processing, and autonomous systems.

The Dell VFJ45 version integrates seamlessly with Dell servers, ensuring efficient thermal management and reliable performance in rack-mounted configurations. The passive cooling mechanism leverages system-level airflow, making it ideal for enterprise-scale deployments.

Tensor Core Technology

Tensor Cores are at the heart of the V100’s AI acceleration. These specialized cores perform mixed-precision matrix multiplications and accumulations in a single operation. This allows the V100 to deliver up to 12 times higher training performance compared to previous generations. The result is a major leap in efficiency for neural network training and inference tasks across diverse industries.

Parallel Processing Efficiency

With 5120 CUDA cores, the Tesla V100 handles multiple concurrent threads, optimizing workload distribution. Its ability to perform parallel calculations accelerates simulations, scientific computations, and machine learning workloads. For research and academia, this capability enables the execution of large-scale data analysis, simulations, and predictive modeling faster than CPU-only systems.

High-Performance Computing (HPC) Acceleration

The Tesla V100 GPU supports floating-point precision computing at FP64, FP32, and FP16 levels. This versatility makes it suitable for scientific simulations, numerical analysis, and engineering workloads. It can execute up to 7.8 teraflops of double-precision (FP64) performance, providing unmatched precision for physics, chemistry, and financial simulations.

Accelerated Mixed-Precision Workloads

The Volta GPU supports mixed-precision arithmetic, combining FP16 and FP32 computations for optimized performance. This approach enhances efficiency without compromising accuracy, which is vital in AI-driven scientific research and high-performance analytics.

Memory and Bandwidth Capabilities

16GB HBM2 Memory

The Tesla V100’s 16GB HBM2 (High Bandwidth Memory 2) ensures fast data transfer between the GPU and memory modules. With a bandwidth of 900 GB/s, the accelerator can handle massive datasets and models efficiently. This high-speed memory helps eliminate bottlenecks, ensuring seamless data access and reduced latency during complex computations.

Advantages of HBM2 Over GDDR5

HBM2 memory offers significant advantages over traditional GDDR5 or GDDR6 memory technologies. It delivers higher bandwidth, lower power consumption, and a smaller physical footprint. These features make the V100 suitable for high-density data center environments, optimizing both performance and energy efficiency.

PCIe 3.0 x16 Interface

Scalable Data Center Deployment

The PCIe 3.0 interface enables system scalability by allowing multiple GPU accelerators within a single node. Enterprises can easily expand computational capabilities by adding more V100 GPUs to their servers, thereby enhancing parallel processing power for distributed computing frameworks such as CUDA, OpenCL, and HPC clusters.

Energy Efficiency and Thermal Design

Despite its 250W TDP, the Tesla V100 offers one of the best performance-per-watt ratios in the industry. The passive cooling design ensures efficient heat dissipation through the server’s airflow system, eliminating the need for dedicated GPU fans. This design reduces mechanical complexity and enhances reliability for 24/7 data center operation.

Optimized for Dell PowerEdge Systems

The Dell VFJ45 version of the Nvidia Tesla V100 GPU is specifically validated for Dell PowerEdge servers. These servers offer optimized BIOS and firmware configurations that maximize GPU performance and thermal stability. The seamless integration allows data centers to deploy multiple accelerators without compatibility concerns.

Multi-GPU and NVLink

Though this specific model uses PCIe rather than NVLink, Dell PowerEdge systems can still deploy multiple V100 GPUs in tandem for enhanced performance. Multi-GPU setups enable massive parallelism for workloads such as model training, rendering, and HPC simulations, delivering exponential performance gains.

Computational Fluid Dynamics (CFD)

CFD simulations require extensive floating-point calculations. The Tesla V100’s FP64 capabilities and memory bandwidth make it ideal for simulating fluid flow, heat transfer, and aerodynamics with precision and speed, aiding researchers and engineers in optimizing product designs and performance.

Data Center and Cloud Integration

Enterprise Data Centers

The Dell VFJ45 Tesla V100 is widely deployed in enterprise data centers for AI training, HPC simulations, and data analytics. Its passive cooling and PCIe interface make it compatible with dense server environments. The energy-efficient design ensures reliable 24/7 operation without performance degradation.

Scalable Deployment Options

Data centers can deploy multiple Tesla V100 GPUs across nodes, forming powerful compute clusters. These clusters deliver petaflops of performance for distributed workloads, improving resource utilization and reducing infrastructure costs.

Cloud-Based GPU Acceleration

Cloud providers like AWS, Google Cloud, and Microsoft Azure use Tesla V100 GPUs to power their AI and HPC services. Enterprises can access these GPUs on demand, scaling their workloads without investing in on-premises infrastructure. This flexibility enhances productivity and reduces capital expenditure.

Enterprise-Grade Durability

The Dell VFJ45 Nvidia Tesla V100 is engineered for long-term stability under intensive workloads. Its passive cooling system reduces mechanical wear, while its efficient thermal design ensures consistent performance. This makes it suitable for mission-critical applications that demand reliability and uptime.

Optimized Power Efficiency

The 250W TDP rating ensures optimal balance between performance and power usage. Advanced power management features dynamically adjust GPU frequency and voltage, maximizing efficiency without compromising performance.

Comparison with Other GPU

Nvidia Tesla P100

Compared to the previous-generation Tesla P100, the V100 offers significant improvements in Tensor Core performance, memory bandwidth, and overall computational efficiency. It provides up to 2.5x performance gains in deep learning workloads, making it the preferred option for modern AI frameworks.

Nvidia Tesla A100

Though the newer Tesla A100 uses the Ampere architecture and provides even greater performance, the Tesla V100 remains highly relevant for organizations seeking cost-effective AI and HPC acceleration. The V100 offers excellent performance per dollar, especially for mid-sized enterprises and research institutions.