A100 PCIe 40GB vs H100 NVL

H100 NVL outperforms A100 PCIe 40GB across key compute precisions, enabling faster AI training and inference. The H100 NVL's 1979 TFLOPS FP16 dwarfs A100's 312 TFLOPS, accelerating mixed-precision training for deep neural networks by up to 6x in theoretical throughput. FP32 performance jumps from 19.5 TFLOPS to 67 TFLOPS, benefiting scientific simulations and single-precision workloads that demand numerical stability.

Memory differences profoundly impact real-world usage: H100 NVL's 94 GB HBM3 supports batch sizes 2.35x larger than A100's 40 GB HBM2e, reducing overhead in large language model training. Bandwidth of 3350 GB/s versus 2039 GB/s minimizes data starvation, sustaining peak FLOPS during memory-intensive operations like transformer inference.

Power draw reflects these gains: H100 NVL at 700W TDP versus A100's 400W requires denser cooling but yields efficiency in tokens per watt for modern FP8 workloads at 3958 TFLOPS.

Select NVIDIA A100 PCIe 40GB for cost-sensitive deployments with moderate model sizes. At $0.60 per hour starting price and 40 GB VRAM, it handles fine-tuning of models under 30 billion parameters efficiently, matching 312 TFLOPS FP16 needs without H100 overhead.

Legacy Ampere-optimized software or PCIe 4.0 environments favor A100, especially where 400W TDP fits power budgets and 2039 GB/s bandwidth suffices for batch sizes up to 128 in inference.

NVIDIA H100 NVL suits demanding AI tasks requiring maximum scale. Its 94 GB HBM3 VRAM accommodates models exceeding 70 billion parameters, with 3350 GB/s bandwidth enabling batch sizes over 256 for high-throughput training.

Hopper's 1979 TFLOPS FP16 and FP8 support at 3958 TFLOPS excel in cutting-edge LLM development, justifying $1.40 per hour pricing for 3-6x speedups despite 700W TDP.