
Subscribe To Our Newsletter!
Subscribe to the newsletter to stay up to date with the latest news and most useful
Newsletter
↑
Back to Top
Product Overview |
|---|
The Cisco NVIDIA Tesla V100-PCIe 32GB HBM2 GPU Accelerator offers powerful GPU processing tailored for AI and high-performance computing tasks. It fits into server systems using PCIe slots and is designed to handle intensive workloads that require fast, high-capacity memory and efficient compute power. |
General Information |
|
|---|---|
| Brand | Cisco |
| Part Number | 30-100222-01 |
| Series | NVIDIA Tesla V100 (V100-PCIe, 32GB HBM2) |
Miscellaneous |
|
|---|---|
| Compliance Standards | RoHS, CE, FCC |
Physical Characteristics |
|
|---|---|
| Weight | 3.00 |
| Condition | Refurbished |
Product Description |
|---|
The Cisco NVIDIA Tesla V100-PCIe 32GB HBM2 GPU Accelerator is built for demanding computing tasks that require significant processing power. It’s often found in data centers and research labs where AI training, scientific simulations, and complex analytics run continuously. Professionals working in machine learning, data science, and high-performance computing benefit from its robust capabilities. Key Features
This accelerator is typically deployed in environments where large datasets and complex computations are routine. It helps speed up training times and improves throughput for AI models, enabling researchers and engineers to iterate faster and achieve better results. |
Use Cases |
|---|
The Cisco NVIDIA Tesla V100-PCIe 32GB HBM2 GPU Accelerator is primarily deployed in high-density data center environments where AI model training and complex simulations require substantial computational resources. Enterprise IT teams and HPC clusters benefit from its ability to accelerate deep learning workflows and analytics workloads that demand both speed and large memory capacity. How It's Used:
This GPU accelerator enhances operational efficiency by enabling parallel processing of demanding workloads, while its PCIe compatibility ensures integration into existing server architectures. Its high memory bandwidth and capacity support scalability for growing AI and HPC demands in enterprise infrastructures. |