A100 vs Gaudi 2

The dense FP16 performance of 312 TFLOPS on the A100 enables certain training scenarios while the FP32 figure of 19.5 TFLOPS supports precision sensitive operations. Memory bandwidth differences mean the Gaudi 2 at 2450 GB/s can support larger batch sizes than the A100 at 2039 GB/s in memory bound applications. The A100 also lists dense INT8 performance at 624 TOPS which provides a reference point absent from Gaudi 2 specifications. Since many Gaudi 2 metrics remain not published by vendor direct performance ratios require caution. The higher TDP of 600W on the Gaudi 2 versus 400W on the A100 affects power efficiency calculations in dense deployments.

Choose the A100 for anything built on CUDA, which is nearly all training and inference code, and for multi-GPU work that uses NVLink inside a node and InfiniBand across nodes. Its published 312 TFLOPS dense FP16, 624 TFLOPS with sparsity, and 624 TOPS INT8 give you predictable throughput, and its 9.7 TFLOPS FP64 covers HPC codes for which the Gaudi 2 lists no figure.

Choose the Gaudi 2 when the model needs the full 96 GB on a single accelerator, which is 20% more than the largest 80 GB A100, and when your pipeline already runs on Intel's Gaudi software stack. Its 2450 GB/s bandwidth is 20% above the 2039 GB/s of the A100, which helps memory bound inference. Expect to port custom kernels and check framework support before committing, since the Gaudi 2 does not run CUDA.