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- In Stock: 26
- Manufacturer: Dell
- Model or Part: T39XP
- 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 T39XP Tesla V100 Nvidia 32GB Graphics Accelerator PCIe delivers world class performance for AI, deep learning and high performance computing workloads. Built on the NVIDIA Volta architecture, this accelerator provides massive parallel processing power in a single double width PCI Express card that can be installed in standard server chassis.
Key Features
- 32 GB of HBM2 memory with 900 GB/s memory bandwidth
- 5120 CUDA cores and 640 Tensor Cores for accelerated matrix operations
- FP64 performance up to 7 TFLOPS, FP32 up to 14 TFLOPS and Tensor performance up to 112 TFLOPS
- PCIe Gen3 x16 interface for high bandwidth data transfer
- NVLink compatible for multi-GPU scaling
- Energy efficient design with dynamic power management
Technical Specifications
- Manufacturer: Dell
- Model / Part Number: T39XP
- Form factor: Double width, full height PCIe card
- GPU architecture: NVIDIA Volta
- Memory capacity: 32 GB HBM2
- Memory bandwidth: 900 GB/s
- CUDA cores: 5120
- Tensor Cores: 640
- Peak FP64 performance: 7 TFLOPS
- Peak FP32 performance: 14 TFLOPS
- Peak Tensor performance: 112 TFLOPS
- Power consumption: 250 W typical
- Operating temperature range: 0 to 85 degrees C
- Interface: PCIe Gen3 x16, supports NVLink bridge
Typical Applications
- Deep learning model training and inference at scale
- Scientific simulation and computational fluid dynamics
- Financial risk modeling and quantitative analysis
- Large scale data analytics and graph processing
- High performance rendering and visual effects
- AI accelerated database queries and recommendation engines
This Dell Tesla V100 accelerator is engineered for enterprises that demand the fastest possible time to insight from massive data sets. By leveraging its high memory capacity and tensor acceleration, users can reduce training cycles, improve model accuracy and achieve unprecedented throughput in demanding workloads.