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- In Stock: 16
- Manufacturer: Dell
- Model or Part: NWWWX
- Condition: Refurbished
- Weight: 3.00lb
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Item Condition
New - Sealed or Open Box ConditionExcellent - Almost-New, Missing Original Packaging
Refurbished - Seller Refurbished, Good Condition, Previously Used Item
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Overview
The Dell NWWWX Tesla V100 Nvidia 32GB 300w GPU SXM2 is a high-performance accelerator designed for demanding AI, deep learning and scientific computing workloads. Built on NVIDIA''s Volta architecture, this board delivers massive parallel processing power while maintaining energy efficiency in the compact SXM2 form factor. Ideal for data centers, research labs and enterprise AI clusters, the Tesla V100 enables faster model training, inference and large-scale simulations.
Key Features
- 32 GB of high-bandwidth HBM2 memory for rapid data access
- 300 W TDP optimized for dense server deployments
- Volta architecture with 5120 CUDA cores and 640 Tensor cores
- NVLink support for multi-GPU scaling and increased bandwidth
- Enhanced FP16, BFLOAT16, INT8 and INT4 precision for AI workloads
- Integrated NVIDIA GPU Direct RDMA for low latency data transfer
Technical Specifications
- Model/Part Number: NWWWX
- Form Factor: SXM2 (compatible with Dell PowerEdge servers)
- GPU Clock Speed: up to 1.53 GHz boost
- Memory Capacity: 32 GB HBM2
- Memory Bandwidth: 900 GB/s
- CUDA Cores: 5120
- Tensor Cores: 640
- Power Consumption: 300 W (max)
- Operating Temperature Range: 0 to 85 degrees Celsius
- Interface: PCIe Gen3 x16 with NVLink support
Applications
- Deep learning model training and inference at scale
- High performance computing (HPC) simulations and scientific research
- Data analytics, big data processing and accelerated database queries
- Computer vision, natural language processing and speech recognition tasks
- Financial modeling, risk analysis and quantitative simulations
- Virtual reality rendering and real-time graphics acceleration
This Dell Tesla V100 GPU provides the compute density and flexibility needed to push AI initiatives forward while fitting seamlessly into existing server infrastructures.