Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
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Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
The Tesla V100 delivers unparalleled precision and speed using Tensor Core technology.
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
商品＃: 194792197

Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing

商品＃: 194792197

TWD 45119

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立即訂購並活動它 週二, 九月 29
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The Tesla V100 delivers unparalleled precision and speed using Tensor Core technology.
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What Stands Out

High Memory
The Tesla V100 features 32GB of HBM2 memory, providing exceptional bandwidth for AI computations, ensuring faster processing and handling of large datasets, ideal for demanding machine learning workloads.
AI Optimized
Specifically designed for AI and deep learning tasks, the Tesla V100 accelerates training and inference, outpacing traditional GPUs and empowering researchers to achieve superior performance in their models.
Flexible Integration
Equipped with a PCIe adapter and a 6+2 pin connector, the Tesla V100 facilitates easy integration into existing systems, making it a versatile choice for both data centers and personal computing setups.

產品詳情

Shop Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing online at a best price in Taiwan. 18316764773
  • GPU Name:GV100 Process Size:12 nm Bus Interface:PCIe 3.0 x16 Base Clock:1230 MHz Boost Clock:1380 MHz Memory Clock:876 MHz Slot Width:Dual-slot TDP:250 W Suggested PSU:600 W Power Connectors:2x 8-pin Memory Size:32 GB Memory Type:HBM2 Memory Bus:4096 bit DirectX:12 (12_1)
GraphicsV100
Graphics processorV100
Ram memory32 GB
BrandASPIRING
Height4 in (10.16 cm)
Model nameV100
Weight1.3 lb (590 grams)
AccessoriesVIDEO CARD

Who Should Buy?

Suitable For
  • AI Researchers

    Perfect for AI researchers needing high processing power for deep learning models and complex computations.

  • Data Scientists

    Ideal for data scientists handling large datasets and performing extensive data analysis with accelerated computing.

  • Supercomputing Centers

    Beneficial for supercomputing centers that require advanced GPU capabilities for numerous simultaneous high-demand tasks.

Not Suitable For
  • Casual Gamers

    Not suitable for casual gamers who don't require the high performance and specifications of this GPU.

產品描述

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客戶問題與解答

  • 問題: What type of workloads is the Tesla V100 designed for?

    Answer: It is optimized for artificial intelligence workloads, including deep learning and high-performance analytics.
  • 問題: How much memory does the Tesla V100 have?

    Answer: It boasts an impressive 32 GB HBM2 memory configuration.
  • 問題: Is the Tesla V100 energy efficient?

    Answer: Yes, it is engineered for exceptional power savings while maintaining top-tier performance.

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TWD 45119

立即訂購並活動它 週二, 九月 29

QTY:

庫存僅剩12件。
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PCI DSS compliant and ISO 27001:2022 certified, with encrypted payments and full buyer protection on every order.

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特色和優勢

  • Optimized for AI workloads with high-precision performance.
  • Versatile for deep learning, analytics, and more across various sectors.
  • Features 32 GB HBM2 memory for handling large data sets effortlessly.
  • Energy-efficient, lowering operational costs and carbon footprint.
  • Supports scalable deployments with built-in NVLink for multi-GPU setups.
  • Designed for enterprise-level solutions with exceptional throughput.

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