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Item #:
194792197
Tesla V100 32GB SXM2 GPU W/ Pcie Adapter & 6+2 Pin For AI Computing
Item #:
194792197
MNT 4653679
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Only 12 items left in stock.
Order now and get it around Saturday, September 26
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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.
Product Details
- 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)
| Graphics | V100 |
| Graphics processor | V100 |
| Ram memory | 32 GB |
| Brand | ASPIRING |
| Height | 4 in (10.2 cm) |
| Model name | V100 |
| Weight | 1.3 lb (590 grams) |
| Accessories | VIDEO CARD |
Who Should Buy?
-
AI Researchers
Perfect for AI researchers needing high processing power for deep learning models and complex computations.
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Data Scientists
Ideal for data scientists handling large datasets and performing extensive data analysis with accelerated computing.
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Supercomputing Centers
Beneficial for supercomputing centers that require advanced GPU capabilities for numerous simultaneous high-demand tasks.
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Casual Gamers
Not suitable for casual gamers who don't require the high performance and specifications of this GPU.
Product Description
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Customer Questions & Answers
-
Question:
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. -
Question:
How much memory does the Tesla V100 have?
Answer: It boasts an impressive 32 GB HBM2 memory configuration. -
Question:
Is the Tesla V100 energy efficient?
Answer: Yes, it is engineered for exceptional power savings while maintaining top-tier performance.
ASPIRING Graphics Cards Editorial Review
The Tesla V100 32GB SXM2 GPU with PCIe Adapter & 6+2 Pin for AI Computing is designed for high-performance computing tasks, featuring a powerful V100 Graphics processor and an impressive 32 GB of RAM memory. With its compact height of 4 inches and weight of 1.3 lbs, it provides efficient processing without taking up much space. Users appreciate its reliability and performance in AI applications, making it a popular choice for engineers and developers alike. This GPU supports various AI workloads, further enhancing its utility in machine learning and deep learning projects.
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Pros
- Powerful V100 Graphics processor for demanding tasks
- 32 GB RAM enhances data processing capabilities
- Compact design saves valuable workspace
- Lightweight for easier installation and handling
- Designed specifically for AI computing tasks
Cons
- May require additional cooling for optimal performance
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MNT 4653679
Order now and get it around Saturday, September 26
This item is not restrict in my country.(Please click on above link if this item is not restrict in your country, So our team will review and allow.)
QTY:
Only 12 items left in stock.
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Features & Benefits
- 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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