Description
The Cisco UCS NVIDIA H100 NVL GPU Accelerator is a high-performance computing module designed for demanding AI, machine learning, and data analytics workloads. Built on the NVIDIA Hopper architecture, this accelerator delivers exceptional compute density, high-bandwidth memory, and advanced GPU-to-GPU interconnects to support large-scale model training and inference.
Optimized for Cisco UCS rack servers, the H100 NVL GPU enables enterprises to accelerate generative AI, deep learning, scientific computing, and high-performance workloads while maintaining centralized management and enterprise-grade reliability. Its NVLink-enabled design allows for fast, low-latency communication between GPUs, making it ideal for distributed AI pipelines and large model deployments in modern data centers.
| Specification | Details |
|---|---|
| Product Type | GPU Accelerator Module |
| GPU Model | NVIDIA H100 NVL |
| Architecture | NVIDIA Hopper |
| GPU Memory | High-bandwidth HBM3 |
| Interconnect | NVIDIA NVLink |
| Form Factor | PCIe GPU Module |
| Compute Focus | AI, Machine Learning, HPC, Data Analytics |
| Deployment | Cisco UCS Rack Servers |
| Cooling | Server-managed active cooling |
| Compatibility | Cisco UCS GPU-enabled platforms |
| Use Cases | Generative AI, LLM training, inference, HPC simulations |
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Return Policy (Short Summary)
We offer a 30-day return window for most unopened, factory-sealed items.
Returns must be authorized before shipping back and must include all original packaging and accessories. Opened or used items are not returnable unless defective.
Some products—such as custom orders, special-order items, audio gear, software, and LED video walls—are non-returnable. Non-defective returns may be subject to a 20–30% restocking fee, and customers are responsible for return shipping costs.
For damaged or incorrect items, please contact us immediately.
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This product is rarely deployed alone. It’s typically part of a fully integrated AV system. AVENDOR designs, integrates, and supports end-to-end environments—from single rooms to enterprise-wide deployments.
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