NVIDIA A100 SXM4 80GB vs NVIDIA A2 16GB

The A2 is the newer part: Ampere, launched in 2021, against the A100's Ampere from 2020. Newer architectures typically add lower-precision formats and better throughput per watt, so check the precision rows your workload actually uses.

Dense throughput for the A100 against the A2: FP32 20 vs 4.5 TFLOPS, FP16 312 vs 18 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 80 GB against 16 GB, fed at 2.0 TB/s versus 200 GB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.

Power budgets are 400 W for the A100 and 60 W for the A2. At FP32 that works out to 0.05 against 0.07 TFLOPS per watt.

For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.