A100 PCIe 40GB vs H200 SXM

The H200 outperforms the A100 dramatically in compute throughput, which translates to faster model training and inference. FP16 performance reaches 1979 TFLOPS on the H200 compared to 312 TFLOPS on the A100: this benefits mixed-precision training common in deep learning, reducing epochs from days to hours for large neural networks. FP32 at 67 TFLOPS versus 19.5 TFLOPS supports scientific simulations and precise inference tasks more efficiently on the H200.

Memory specifications reshape real-world scalability. The H200's 141 GB HBM3e VRAM dwarfs the A100's 40 GB HBM2e, allowing single-GPU deployment of models exceeding 70 billion parameters without sharding. Bandwidth of 4800 GB/s on the H200 versus 2039 GB/s on the A100 sustains larger batch sizes during training, minimizing overhead from data loading and enabling higher throughput in inference servers.

Power consumption reflects these advances: the H200's 700W TDP demands robust cooling compared to the A100's 400W, yet yields up to 6x FP16 uplift. FP8 at 3958 TFLOPS on the H200 accelerates quantized inference for deployment-scale serving.

The A100 PCIe 40GB suits budget-conscious deployments where cloud pricing from $0.60/hr and average $1.85/hr provides value. It excels in workloads fitting within 40 GB HBM2e VRAM, such as fine-tuning mid-sized models or Stable Diffusion generation, without needing the H200's higher $1.19/hr starting cost.

Lower 400W TDP makes the A100 preferable in power-limited environments or PCIe slots, while PCIe 4.0 interconnect supports legacy clusters optimized for Ampere architecture.

The H200 SXM dominates for memory-intensive tasks leveraging 141 GB HBM3e VRAM and 4800 GB/s bandwidth. It handles massive LLMs or high-resolution simulations on a single GPU, avoiding A100's multi-node complexity for 40 GB limits.

Superior compute at 1979 TFLOPS FP16 and 3958 TFLOPS FP8 accelerates training and quantized inference, justifying $3.70/hr average pricing for production-scale AI pipelines.