Cisco HCI-GPU-A100-80M6 | A100 80GB GPU Accelerator, 300W PCIe 4.0, Passive

Q: What workloads is the A100 80GB optimized for?
A: The A100 80GB is optimized for large-scale AI training, deep learning inference, natural language processing, recommendation systems, high-performance computing simulations, and data analytics. The 80GB memory capacity enables training of large language models and neural networks with billions of parameters, while Multi-Instance GPU technology allows efficient multi-tenant deployment across diverse workloads.

Q: How does Multi-Instance GPU (MIG) work on the A100?
A: MIG allows the A100 80GB to be partitioned into up to seven isolated GPU instances, each with dedicated memory, cache, compute cores, and memory bandwidth. Each 10GB instance operates independently with guaranteed quality of service, enabling multiple users or workloads to share a single GPU efficiently. MIG is compatible with Kubernetes, containers, and virtualization platforms.

Q: What are the PCIe slot requirements for this GPU?
A: The HCI-GPU-A100-80M6 requires a PCIe 4.0 x16 slot for optimal performance. It is a dual-slot, full-height, full-length card with passive cooling, requiring adequate server chassis airflow. In Cisco UCS C-series servers, it can be installed in designated GPU-capable riser slots with x16 support.

Q: Can multiple A100 GPUs be linked together?
A: Yes, up to two A100 PCIe GPUs can be connected using an NVIDIA NVLink Bridge for direct GPU-to-GPU communication. For larger multi-GPU configurations with higher bandwidth interconnects, the SXM4 variant with NVLink and NVSwitch is recommended for HGX and DGX platforms.

Q: What precision formats does the A100 support?
A: The A100 supports a wide range of precision formats including FP64 (double precision), FP32 (single precision), TF32 (TensorFloat-32 for AI), FP16 (half precision), BFloat16, INT8, and INT4. Third-generation Tensor Cores accelerate mixed-precision training and inference, with support for structured sparsity to double performance on compatible models.