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- In Stock: 1
- Manufacturer: NVIDIA
- Model or Part: 900-22081-0155-000
- Condition: Refurbished
- Weight: 3.00lb
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Refurbished - Seller Refurbished, Good Condition, Previously Used Item
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Manufacturer: NVIDIA
Model/Part Number: 900-22081-0155-000
Product Name: Tesla K40 12GB PCIe 3.0 GPU
The NVIDIA Tesla K40 is a high-performance compute accelerator designed for scientific computing, deep learning research and large scale data analytics. Built on the Kepler architecture, this PCIe 3.0 card delivers massive parallel processing power while maintaining energy efficiency, making it an ideal choice for demanding workloads in research labs and enterprise data centers.
Key Features
- 12 GB GDDR5 memory with a 384-bit interface for high bandwidth data access
- 2880 CUDA cores provide fast parallel execution of complex algorithms
- PCIe Gen3 x16 interface ensures maximum throughput between the GPU and host system
- GPU Boost technology automatically raises clock speeds to meet workload demands
- ECC memory support for increased reliability in mission critical applications
- Supports NVIDIA CUDA, OpenCL and Direct Compute APIs for flexible software development
Technical Specifications
- GPU Architecture: Kepler GK110B
- CUDA Cores: 2880
- Memory Size: 12 GB GDDR5
- Memory Bandwidth: 288 GB per second
- Core Clock: 745 MHz (base) up to 875 MHz (boost)
- Thermal Design Power: 235 W
- Interface: PCI Express 3.0 x16
- Form Factor: Full height, double slot
- Operating Temperature: 0 to 85 degrees Celsius
Typical Applications
- High performance scientific simulations such as molecular dynamics and finite element analysis
- Deep learning training and inference for neural networks
- Big data processing including Hadoop, Spark and GPU-accelerated databases
- Computer aided engineering (CAE) workloads like CFD and structural analysis
- Financial modeling and risk assessment that require fast parallel calculations
The Tesla K40 combines robust memory capacity with exceptional compute capability, delivering the performance needed to accelerate complex algorithms and reduce time-to-insight across a wide range of computational fields.