A40 vs H100 SXM5

The H100 SXM5 vastly outpaces the A40 in FP16 performance at 1979 TFLOPS compared to 37.4 TFLOPS, accelerating deep learning training where half-precision computations dominate. This disparity translates to training large neural networks up to 50 times faster on the H100, reducing iteration times significantly. FP32 performance shows a narrower gap, with H100 at 67 TFLOPS over A40's 37.4 TFLOPS, benefiting general-purpose computing and simulations equally. The H100's FP8 capability of 3958 TFLOPS enhances inference efficiency for quantized models, unavailable on the A40. Memory bandwidth defines practical limits: H100's 3350 GB/s versus 696 GB/s supports larger batch sizes in training, enabling models with billions of parameters without swapping to host memory. Consequently, inference latency drops for high-throughput serving on H100. Higher TDP of 700W on H100 demands robust cooling, unlike A40's 300W, impacting deployment density.

The A40 suits cost-sensitive deployments where cloud pricing starts at $0.24 per hour, averaging $1.31 per hour across 23 offers, far below H100 SXM5's $0.80 to $3.54 per hour. Its PCIe form factor and 300W TDP integrate easily into existing servers without power upgrades. Choose A40 for inference on models fitting within 48 GB GDDR6 or FP32-heavy scientific tasks leveraging 37.4 TFLOPS.

Opt for H100 SXM5 in high-performance AI training requiring 1979 TFLOPS FP16 or 80 to 94 GB HBM3 VRAM for massive datasets. Its 3350 GB/s bandwidth handles large batch sizes critical for stable LLM fine-tuning. SXM5 form factor with NVLink and InfiniBand excels in multi-GPU clusters for scalable computing at 3958 TFLOPS FP8 inference.