A40 vs H200

The H200 dominates in compute performance: its FP16 capability reaches 1979 TFLOPS compared to the A40's 37.4 TFLOPS, enabling over 50 times faster half-precision operations critical for AI training and inference. FP32 performance also favors the H200 at 67 TFLOPS versus 37.4 TFLOPS, providing nearly double the single-precision throughput for scientific simulations. The H200's FP8 support at 3958 TFLOPS further accelerates inference tasks on quantized models. Memory specifications amplify these advantages: 141 GB HBM3e VRAM supports massive models that exceed the A40's 48 GB GDDR6 limit, while 4800 GB/s bandwidth versus 696 GB/s allows larger batch sizes and reduces data bottlenecks in deep learning pipelines. Higher TDP of 700W on the H200 reflects its power demands compared to 300W on the A40, necessitating robust cooling and power infrastructure. Overall, these specs translate to dramatically faster training times and scalable inference for the H200 in memory-intensive workloads.

The A40 suits cost-sensitive projects where workloads fit within 48 GB GDDR6 VRAM and 696 GB/s bandwidth. Its lower TDP of 300W and PCIe form factor integrate easily into standard servers without specialized infrastructure. At average cloud pricing of $1.31 per hour from $0.24 per hour across 23 offers, it delivers value for smaller-scale AI inference, visualization, or legacy Ampere-optimized applications that do not require Hopper's advancements.

Opt for the H200 in scenarios demanding extreme scale: 141 GB HBM3e VRAM handles the largest language models, and 4800 GB/s bandwidth supports high-throughput training with large batches. FP16 at 1979 TFLOPS and FP8 at 3958 TFLOPS excel in modern AI pipelines. Despite 700W TDP and SXM/NVL form factors with NVLink/PCIe 5.0/InfiniBand, its average $3.62 per hour pricing from $0.50 per hour across 26 offers justifies premium performance in data centers.