A100 SXM4 40GB vs H200 SXM

The H200 surpasses the A100 dramatically in compute throughput: FP16 performance reaches 1979 TFLOPS versus 312 TFLOPS, accelerating mixed-precision training for deep learning models by over six times. FP32 capability climbs to 67 TFLOPS from 19.5 TFLOPS, benefiting scientific simulations and graphics rendering that rely on single-precision arithmetic. The FP8 rating of 3958 TFLOPS on H200 further optimizes inference for quantized large language models, reducing latency in deployment scenarios.

Memory specifications define real-world usability: H200's 141 GB HBM3e VRAM versus A100's 40 GB HBM2e enables handling models exceeding 100 billion parameters without multi-GPU sharding. Bandwidth of 4800 GB/s compared to 2039 GB/s supports larger batch sizes, minimizing data transfer bottlenecks during training and allowing throughput increases of up to 2.4 times. These deltas translate to faster convergence in LLM training and higher inference queries per second, though H200's 700W TDP demands robust cooling versus A100's 400W.

Opt for the A100 SXM4 40GB in cost-sensitive environments where models fit within 40 GB VRAM, such as fine-tuning mid-sized transformers or Stable Diffusion pipelines. Its average cloud price of $2.63 per hour undercuts H200's $3.70 per hour, yielding savings for prolonged workloads. Lower 400W TDP suits power-constrained clusters, and PCIe 4.0 interconnect maintains compatibility with legacy InfiniBand setups.

Select the H200 SXM for memory-bound tasks like training or inferring LLMs over 70 billion parameters, leveraging 141 GB HBM3e VRAM to avoid fragmentation. Superior 4800 GB/s bandwidth sustains massive batch sizes, enhancing throughput in scientific computing. Despite 700W TDP, PCIe 5.0 and broader availability across 23 cloud offers justify the $3.70 per hour average for high-utilization production.