A40 vs H100

The H100 dominates in raw compute: its 1979 TFLOPS FP16 dwarfs the A40s 37.4 TFLOPS, accelerating deep learning training where half-precision dominates. For FP32 tasks like simulations, H100s 67 TFLOPS edges out A40s 37.4 TFLOPS, though A40 maintains parity in balanced FP16/FP32 ratios ideal for legacy codes. The FP16/FP32 delta means H100 excels in modern training pipelines using mixed precision, reducing epochs by factors tied to its 53x FP16 advantage.

Memory specs transform workloads: H100s 3350 GB/s bandwidth versus A40s 696 GB/s supports vastly larger batch sizes in inference and training, minimizing out-of-memory errors for models exceeding 48 GB. This enables handling LLMs up to 94 GB contexts on H100, while A40 suits smaller models. Higher bandwidth on H100 cuts data starvation, boosting effective throughput by enabling 4.8x faster memory access.

Power efficiency shifts with scale: A40s 300W TDP yields solid performance per watt for lighter loads, but H100s 700W unlocks peak Hopper tensor cores for FP8 inference at 3958 TFLOPS, slashing latency in production serving.

The A40 suits budget-conscious deployments requiring reliable performance without extreme scale. At from $0.24 per hour versus H100s $0.80 per hour, it offers 48 GB GDDR6 VRAM for fine-tuning mid-sized models or Stable Diffusion generation where 37.4 TFLOPS FP16 suffices. Its 300W TDP fits power-limited cloud instances or PCIe slots, avoiding H100s 700W demands.

Choose the H100 for cutting-edge AI workloads demanding top throughput. Its 80-94 GB HBM3 and 3350 GB/s bandwidth handle massive LLMs in training or inference, with 1979 TFLOPS FP16 enabling faster convergence than A40s 37.4 TFLOPS. Despite higher from $0.80 per hour pricing, interconnects like PCIe 5.0 and InfiniBand scale clusters efficiently.