Original NVIDIA Tesla V100 GPU card: PCIe, 16/32 GB HBM2 memory, 300 W TDP. Perfect for AI training, HPC, and deep learning. UAE-wide delivery!
Overview
Unleash blistering compute with the NVIDIA Tesla V100. Whether you choose 16 GB or 32 GB of ultra-fast HBM2 memory, this PCIe GPU card accelerates AI training, scientific simulations, and high-performance computing. Its 300 W thermal envelope and voluminous memory deliver massive parallelism for your most demanding workloads.
Product Description
Built on the NVIDIA Volta™ architecture, the Tesla V100 packs 5 120 CUDA® cores and 640 Tensor cores for 120 TFLOPS of mixed-precision performance. The PCIe form-factor fits any standard server slot, while dual-slot cooling keeps it stable under continuous load. With ECC-protected HBM2 memory and hardware monitoring, it ensures both reliability and performance for data-center and workstation use.
Key Features
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CUDA & Tensor Cores: 5 120 CUDA + 640 Tensor cores
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Memory Options: 16 GB or 32 GB HBM2 ECC-protected
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Interface: PCIe 3.0 x16
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Double Precision: Up to 7.8 TFLOPS FP64
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TDP: 300 W
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NVLink Ready: Scale across multiple GPUs (with NVLink bridge)
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Cooling: Dual-slot active fan for optimal airflow
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Form Factor: Full-height, half-length PCIe card
Specifications
| Spec |
Details |
| Architecture |
NVIDIA Volta™ |
| CUDA Cores |
5 120 |
| Tensor Cores |
640 |
| Memory Bandwidth |
900 GB/s |
| Memory Type |
HBM2 ECC |
| Memory Capacity |
16 GB or 32 GB |
| Interface |
PCIe 3.0 x16 |
| Double-Precision |
7.8 TFLOPS |
| Form Factor |
Full-height, half-length |
| Power |
300 W TDP |
| Cooling |
Dual-slot active fan |
| OS Support |
Linux, Windows Server |
Supported Applications & Industries
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AI & Deep Learning: TensorFlow, PyTorch, MXNet
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HPC & Simulation: ANSYS, GROMACS, OpenFOAM
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Data Analytics: RAPIDS, MATLAB, SAS
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Industries: Research, finance, healthcare, oil & gas, logistics
Benefits & Compatibility
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Massive Throughput: Tensor cores speed up matrix ops by 12×
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Large Models: Up to 32 GB memory for giant neural nets
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Server-Ready: PCIe plug-and-play in x16 slots
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Scalable: Link multiple cards via NVLink bridges
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Trusted Reliability: ECC memory and hardware monitoring
Purpose of Use
Accelerate AI training, inference, and scientific computation. Ideal for data centers, research labs, or any environment seeking to crush large-scale parallel workloads.
How to Use
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Power down server and unlock PCIe slot latch.
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Insert V100 into a PCIe 3.0 x16 slot until firmly seated.
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Secure bracket and reconnect power cables.
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Boot system and install NVIDIA driver & CUDA toolkit.
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Verify GPU with nvidia-smi and launch your compute jobs.
Packaging / Weight / Dimensions
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Includes: Tesla V100 card, quick-start guide, NVLink covers
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Dimensions: 267 × 111 × 40 mm (L×H×W)
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Weight: ~1.4 kg
Warranty & FAQs
Warranty: 3-year hardware warranty through NVIDIA/HPE
FAQs:
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Can I link cards? Yes—use NVLink bridges for high-bandwidth GPU clusters.
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Driver support? Compatible with R450+ Linux drivers and Windows Server 2016+.
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Power cables? Requires two 8-pin PCIe power connectors.
Performance, Quality, Durability & Reliability
Engineered for 24×7 operation in data-center environments. Comes with thermal sensors, power monitoring, and ECC memory to ensure continuous, error-free operation.
Best Price Guarantee
Found a lower price on an identical Tesla V100 card? We’ll match it—shop with confidence.
Shop Today & Receive Your Delivery Across the UAE!
Fast dispatch to Dubai, Abu Dhabi, Sharjah, and beyond. Single or bulk orders ship within 24 hrs.
Request for Customer Reviews and Testimonials
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After-Sales Support
Our GPU experts are on call 24/7 via chat, email, or phone for driver help, cluster setup, and troubleshooting.
Get in Touch
Need volume quotes, cluster design assistance, or pro-services? Reach out anytime on our Contact Page.
Stock Availability
Typically in stock for immediate dispatch. For urgent or bulk orders, please confirm availability before checkout.
Disclaimer
Specifications and pricing subject to change without notice. Images are illustrative. Verify final details with our sales team prior to purchase.
