
Dell
Dell PowerEdge C4130 Dual Xeon Server K80 GPU
Renewed high-density GPU server with four NVIDIA K80 accelerators and 1TB RAM for parallel computing workloads.
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Overview
High-Density Computing Power: Dell PowerEdge C4130 (Renewed)
The Dell PowerEdge C4130 offers exceptional computing density for demanding workloads. This renewed server is equipped with dual Intel Xeon processors, massive memory, and fast solid-state storage, making it suitable for high-performance computing applications.
Key Features
- Dual Intel Xeon E5-2609 V3 Six Core Processors
- 1024GB of RAM
- Dual 800GB SSDs
- Four NVIDIA K80 GPUs
Specifications
- Processor: 2 x Intel Xeon E5-2609 V3 Six Core 1.9Ghz
- Memory: 1024GB RAM
- Storage: 2 x 800GB SSD
- Graphics: 4 x NVIDIA K80
- Condition: Renewed
Frequently Asked Questions
What condition does "Renewed" mean for this server?
The server has been professionally inspected, tested, and restored to working condition. It may show minor cosmetic wear but is fully functional and verified to meet original performance specifications.
What types of workloads are the four NVIDIA K80 GPUs suited for?
The K80 GPUs are designed for parallel computing tasks such as deep learning training, scientific simulations, financial modeling, and molecular dynamics. Each K80 card contains two GK210 GPUs, giving this server a total of eight GPU cores.
Does the PowerEdge C4130 include rails for rack mounting?
Rail kits are not typically included with renewed servers. You should verify with the seller whether rack rails are part of this listing or need to be purchased separately.
What is the memory configuration of this server?
The server is equipped with 1024GB (1TB) of RAM, distributed across the memory slots supported by the dual Xeon E5-2609 V3 processors.
Is this server suitable for running current deep learning frameworks?
The K80 GPUs support CUDA and are compatible with frameworks like TensorFlow and PyTorch, though they are an older generation. They are adequate for training smaller models and inference but will be significantly slower than current-generation GPUs for large-scale training.