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- In Stock: 4
- Manufacturer: HP
- Model or Part: 712972-001
- 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 HP part number 712972-001 is the NVIDIA Tesla K20X 6GB Kepler GPU Graphics Accelerator, a high-performance compute card designed for demanding scientific, engineering and data-analytics workloads. Built on the Kepler architecture, this accelerator delivers massive parallel processing power while integrating seamlessly into HP workstations and servers.
Key Features
- 6 GB of GDDR5 memory with 192 bit wide bus for high bandwidth access
- 1312 CUDA cores enable fast parallel execution of complex algorithms
- Peak double precision performance of 1.31 TeraFLOPS for scientific calculations
- Supports NVIDIA CUDA, OpenCL and Direct Compute APIs for flexible development
- Optimized power consumption with a typical draw of 225 Watts
- PCI Express Gen3 x16 interface ensures low latency data transfer
Technical Specifications
- GPU Architecture: Kepler GK110
- CUDA Cores: 1312
- Memory Capacity: 6 GB GDDR5
- Memory Bandwidth: 288 GB/s
- Core Clock: 732 MHz (base)
- Boost Clock: 822 MHz (max)
- Single Precision Performance: 3.95 TeraFLOPS
- Double Precision Performance: 1.31 TeraFLOPS
- Thermal Design Power: 225 W
- Form Factor: Dual slot, full height
- Interface: PCIe Gen3 x16
Typical Applications
- High performance computing (HPC) simulations in physics, chemistry and fluid dynamics
- Machine learning training and inference for deep neural networks
- Large scale data analytics and real-time processing of big datasets
- Computer aided engineering (CAE) tasks such as finite element analysis and computational fluid dynamics
- Financial modeling, risk analysis and quantitative research requiring fast matrix operations
This accelerator brings enterprise-grade GPU performance to HP platforms, enabling researchers, engineers and data scientists to accelerate their most compute-intensive workloads with confidence.