Cisco UCSC-GPU-A30-D | A30 Tensor Core GPU, 24GB HBM2, 180W Passive, PCIe 4.0

Q: What workloads is the UCSC-GPU-A30-D best suited for?
A: This GPU accelerates AI inference at scale, machine learning model training, high-performance computing applications, data analytics, and virtual desktop infrastructure. Multi-Instance GPU technology allows you to partition the A30 into up to four isolated instances, making it ideal for multi-tenant environments and diverse concurrent workloads in enterprise data centers.

Q: Which Cisco UCS servers are compatible with this GPU?
A: The UCSC-GPU-A30-D is designed for Cisco UCS C-Series rack servers including C240 M6 and M7 models. It requires a PCIe 4.0 x16 slot, and the server must support double-wide, 3-slot GPU form factors. Up to three A30 GPUs can be installed in supported servers with appropriate riser configurations and power capacity.

Q: What is Multi-Instance GPU and how does it work?
A: Multi-Instance GPU technology allows a single A30 to be securely partitioned into up to four independent GPU instances, each with dedicated memory, cache, and compute cores. This enables multiple users or workloads to share a single physical GPU with hardware-level isolation, optimizing utilization and cost efficiency while maintaining quality of service for each instance.

Q: Does this GPU require active cooling or special power connectors?
A: The UCSC-GPU-A30-D features passive cooling designed for server airflow environments with a 180W thermal design power. The GPU draws power through the PCIe slot and does not require external power connectors. Ensure your server has adequate airflow and power supply capacity to support the GPU's 180W power consumption.

Q: How does the A30 compare to the A100 for AI workloads?
A: The A30 provides a cost-effective balance for mainstream enterprise servers, offering 24GB memory and strong AI inference performance at lower power consumption than the A100. While the A100 delivers higher peak performance for large-scale training, the A30 excels in multi-tenant environments with MIG support and is optimized for inference workloads, edge AI deployment, and HPC applications that don't require maximum throughput.