You are designing an AI infrastructure cluster and comparing GPU hardware specifications. Which of the following statements is incorrect regarding the technical specifications of the NVIDIA A100 Tensor Core GPU?
Select an answer to reveal the explanation.
Short Explanation
You need to know the A100 uses HBM/HBM2e, not GDDR6, and comes in 40GB or 80GB configs with huge bandwidth. So the GDDR6/24GB claim is the only incorrect statement.
Full Explanation
The incorrect statement is the one saying that the A100 utilizes traditional GDDR6 memory modules with a maximum configuration capacity of 24GB. That statement is the only incorrect statement. The A100 does not use GDDR6, and its memory capacity is not limited to 24GB. It uses High Bandwidth Memory: HBM2 on early 40GB models and HBM2e on 80GB models. HBM is stacked vertically on the silicon interposer next to the GPU die, giving a much wider memory bus, up to 5120-bit, and bandwidth of roughly 1.6 TB/s to 2.0 TB/s. The MIG answer choice is wrong because the statement it contains is true: the A100 does utilize Multi-Instance GPU technology to partition a single physical GPU into up to seven independent hardware instances, with physical hardware isolation for compute and memory. The third-generation Tensor Cores answer choice is wrong because the statement it contains is true: the A100 features third-generation Tensor Cores that support TF32 precision, enabling out-of-the-box speedups for FP32 training workloads without requiring developers to write mixed-precision code. The 80GB HBM2e answer choice is wrong because the statement it contains is true: the A100 supports up to 80GB of HBM2e memory, and that configuration is standard for deep learning training clusters with bandwidth exceeding 1.5 TB/s.