Cisco UCSC-GPU-A100-80 | A100 80GB GPU Accelerator, 300W Passive, MIG-Capable

Q: What workloads is the UCSC-GPU-A100-80 designed for?
A: This GPU accelerator is optimized for AI training and inference, deep learning, natural language processing, recommendation systems, high-performance computing, and data analytics. Its 80GB memory capacity makes it ideal for large language models, massive datasets, and complex neural networks that exceed the memory limits of lower-capacity GPUs.

Q: What is Multi-Instance GPU (MIG) and how does it work?
A: MIG allows the A100 to be securely partitioned into up to seven independent GPU instances, each with dedicated memory, cache, and compute resources. This enables multiple workloads or users to share a single GPU with guaranteed quality of service and isolation, maximizing GPU utilization in multi-tenant or mixed-workload environments.

Q: Which Cisco UCS servers are compatible with this GPU?
A: The UCSC-GPU-A100-80 is compatible with select Cisco UCS C-Series rack servers such as the C240 M6. It requires PCIe 4.0 x16 support and must be installed in specific riser slots (e.g., Riser 1A slot 2, Riser 2A slot 5, or Riser 3C slot 7). Each compatible server can typically support up to three of these GPUs. Cisco UCS systems require GPUs with unique SBIOS IDs, so A100 cards must be procured from Cisco.

Q: What are the power and cooling requirements?
A: The GPU has a 300W thermal design power and uses passive cooling via heatsink. It requires adequate system airflow and PCIe auxiliary power connections from a compatible server chassis. Ensure your UCS server power supply and cooling infrastructure can support the total GPU load if installing multiple accelerators.

Q: How does the 80GB model compare to the 40GB A100?
A: The 80GB variant doubles the memory capacity and offers approximately 1.9 TB/s bandwidth compared to the 40GB model. This enables training of larger models, processing bigger batch sizes, and running more simultaneous MIG instances (each MIG slice gets double the memory). For memory-intensive workloads like large recommendation systems or GPT-class models, the 80GB version can deliver 1.3X to 3X higher throughput.