H100 NVL vs H200 NVL

Compute capabilities match precisely between the H100 NVL and H200 NVL: 1979 TFLOPS FP16 and 3958 TFLOPS FP8 suit mixed-precision training and inference, while 67 TFLOPS FP32 handles general-purpose computing. The H200 NVL excels in memory-intensive scenarios with 141 GB HBM3e versus 80 to 94 GB HBM3, allowing single-GPU loading of models up to 141 GB without sharding, which reduces latency in LLM fine-tuning. This VRAM increase directly supports larger batch sizes, for example, doubling effective throughput in training runs limited by memory. Memory bandwidth jumps from 3350 GB/s to 4800 GB/s in the H200 NVL, minimizing stalls during data transfers and boosting inference speeds by up to 43 percent in bandwidth-bound tasks like token generation. For training, higher bandwidth accelerates gradient computations; for inference, it enables serving larger contexts without throughput drops.

Select the H100 NVL for scenarios prioritizing availability and ecosystem maturity: nine live cloud offers versus four for H200 NVL ensure broader provider options. Its support for PCIe 5.0 alongside NVLink suits hybrid deployments where PCIe form factors integrate easily. Proven deployment since 2022 minimizes risks in production environments handling up to 94 GB models at $1.40 per hour starting price.

Opt for the H200 NVL in memory-constrained workloads: 141 GB HBM3e VRAM handles massive LLMs or high-resolution diffusion models that exceed the H100 NVL's 94 GB limit. The 4800 GB/s bandwidth delivers faster access for large batch inference, starting at $0.50 per hour. Newer 2024 architecture future-proofs against growing model sizes.