Nvidia Tesla v100 16GB

Nvidia Tesla v100 16GB Graphics Card Review

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Nvidia Tesla v100 16GB Graphics Card Review

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These cards range in price from low-cost options for casual gaming to ultra-high-end Quadro-based cards used by graphic designers and researchers. NVIDIA technology is at the core of all of these cards.It was one of the first companies to produce custom graphics cards.

NVIDIA’s Tesla V100 16GB GPU is a double-wide PCI Express card. As the name suggests It has 16GB of good memory capacity isn’t this capacity is amazing to buy. The GV100 graphics processor is a big chip with a die area of 815 mm2 and 21,100 million transistors. It highlights 5120 shading units, 320 texture mapping units, and 128 ROPs. 640 tensor cores are also present, which help machine learning applications to run faster and make all processing faster.

 The Tesla V100 PCIe 16 GB is connected to 16 GB HBM2 memory through a 4096-bit memory interface. The GPU runs at a frequency of 1245 MHz, which may be increased to 1380 MHz, while the RAM runs at 876 MHz.The NVIDIAV100 Tensor Core is the most ADVANCES data center GPU yet created for AI, high-performance computing (HPC), data science, and graphics. It’s based on NVIDIA Volta architecture, has 16 and 32GB of RAM. It can handle the workload of up to 32 CPUs in a single GPU

The NVIDIA Tesla V100 16GB GPU is designed to satisfy the needs of many modern computer systems and is compatible with PCIe slots, the most prevalent form format. When compared to the SXM2 version, which uses NVLink for direct connection with the CPU, the PCIe version has a lower thermal design power point. Deep learning, quantum chemistry, finance, weather, and other applications benefit from the NVIDIA Tesla V100 16GB and 32GB GPUs.

The Tesla V100 was designed to bring AI and HPC together. It provides a platform for HPC systems to excel at both computational research and data science. A single server with Tesla V100 GPUs may replace hundreds of commodity CPU-only servers for typical HPC and AI applications by combining NVIDIA CUDA cores and Tensor Cores inside a unified architecture.

Features:

  • Tesla V100
  • fan less
  • Volta architecture
  • Tensor Core
  • Advanced NV Link
  • HBM2
  • Maximum efficiency mode
  • Improved programmability
  • Base Clock Speed: 1230 MHz
  • 16GB HBM2 GPU Memory
  • Memory Interface: 4096-bit
  • Memory Bandwidth: 897 GB/s
  • 5120.0 CUDA Parallel-Processing Cores
  • 14130 GFLOPS Single-precision compute power
  • System Interface: PCI-E 3.0 x 16
  • Maximum Power Consumption: 300 W
  • Cooling Solution: Passive
  • Output Type: No Outputs

Performance:

The NVIDIA Tesla V100 GPU is powered by Volta and has 21.1 billion transistors on a 12nm technology, allowing it to plough through complicated tasks with ease. It has 640 tensor cores, 5120 shading units, 320 texture mapping units, and 128 ROPs, as well as 5120 shading units, 320 texture mapping units, and 128 ROPs to help increase machine learning application speed. With 250 watts of power coming in via a single 8-pin power connector, this graphics processing unit is the engine of the modern data center, giving groundbreaking performance. Users may get by with fewer servers thanks to the raw computational capacity provided by this card, which results in lower power consumption and total expences.

Memory:

A 4096-bit memory interface connects the NVIDIA V100 16GB to the PCIe bus. The maximum memory clock speed is 877 MHz, with a peak memory bandwidth of up to 900GB/s when equipped with 16GB. Error correction code (ECC) is enabled on the Tesla V100 PCIe boards to protect the GPU’s memory interface and on-board memories. The GPU will retry each memory transaction that has an ECC error until the data transfer is error-free.

Architecture by Volta

A single server with Tesla V100 GPUs can replace commodity CPU servers for traditional HPEC and Deep Learning by combining CUDA Cores and Tensor Cores inside a unified architecture.

Core tensor

Tesla V100 provides 125 Teraflops of deep learning capability thanks to its 640 Tensor Cores. More Tensor FLOPS for DL Inference and Training.

Advances NVLink

In the Tesla V100, NVIDIA NVLink provides increased throughput. Up to eight Tesla V100 accelerators can be connected at high speeds to give a single server the most application performance workable.

HBM2

The Tesla V100 provides increased memory bandwidth thanks to a combination of greater raw bandwidth and higher DRAM utilization efficiency.

Mode of maximum efficiency

Maximum efficiency mode enables data centers to increase computing capacity per rack while staying within their power budget. In this mode, the Tesla V100 operates at maximal processing efficiency, delivering more performance while consuming less power.

Programmability has been improved.

The Tesla V100 was designed with programmability in mind from the very beginning. Its autonomous thread scheduling enables finer-grained synchronization while also enhancing GPU utilization by pooling resources across multiple tiny tasks.

The Tesla V100 PCIe accelerator is cooled using a passive bidirectional heat sink that allows air to flow from left to right. It also includes a migration engine, as well as support for double precision (FP64), single precision (FP32), and half precision (FP64) (FP16). Maximum Performance (Max-P) and Maximum Efficiency (Max-Q) settings, for example, allow for more efficient power consumption.

Max-P mode can operate at full power up to 250W TDP to speed up applications that demand the maximum data throughput and processing performance. In Max-Q mode, administrators can tweak the power usage to get the best performance per watt. A power limit for all GPUs in a rack can also be set via software. Max-Q isn’t predicated on a specific amount of power.

Summary

The NVIDIA Tesla V100 GPU is designed for deep learning, science, engineering, and other applications. The 16GB of HBM2 memory makes it simple to finish projects quickly and affordably. This GPU is suited for modern high-performance data centers because of its integrated security, ease of use, and flexibility.

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