NVIDIA H100 NVL 94GB vs NVIDIA H200 SXM 141GB

The H200 is the newer part: Hopper, launched in 2024, against the H100's Hopper from 2023. Newer architectures typically add lower-precision formats and better throughput per watt, so check the precision rows your workload actually uses.

Dense throughput for the H100 against the H200: FP64 30 vs 34 TFLOPS, FP32 60 vs 67 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 94 GB against 141 GB, fed at 3.9 TB/s versus 4.8 TB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.

Power budgets are 400 W for the H100 and 700 W for the H200. At FP32 that works out to 0.15 against 0.10 TFLOPS per watt.

For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.