B200 NVL vs H200 NVL

The B200 NVL outperforms the H200 NVL significantly in compute capabilities: its 4500 TFLOPS FP16 rating doubles the H200's 1979 TFLOPS, accelerating large model training where mixed-precision computations dominate. FP32 performance edges higher at 90 TFLOPS versus 67 TFLOPS, benefiting scientific simulations requiring single-precision accuracy. FP8 at 9000 TFLOPS on the B200 crushes the H200's 3958 TFLOPS, ideal for inference on quantized models.

Memory specs define real-world scalability: 192 GB VRAM on the B200 supports larger batch sizes than the H200's 141 GB, enabling training of models with billions of parameters without splitting. The 8000 GB/s bandwidth versus 4800 GB/s reduces data bottlenecks, allowing bigger effective batch sizes in LLM inference and faster epoch times in training. Higher TDP of 1000W on the B200 demands robust cooling compared to 700W on the H200, but yields proportional gains in sustained workloads.

These deltas translate to 2.3 times FP16 throughput and 67 percent more VRAM, slashing training times for massive datasets while expanding context windows in generative AI.

The B200 NVL suits demanding AI research and production where peak performance overrides cost: its 4500 TFLOPS FP16 and 192 GB VRAM excel in training LLMs exceeding 100 billion parameters. Deploy it for inference on models requiring 9000 TFLOPS FP8 to handle high-concurrency requests with minimal latency.

Enterprises processing petabyte-scale datasets benefit from 8000 GB/s bandwidth, which sustains large batch sizes unattainable on the H200 NVL.

The H200 NVL fits cost-conscious deployments needing strong Hopper performance: at $2.54 per hour average, it delivers 1979 TFLOPS FP16 for fine-tuning mid-sized models without B200 premiums. Its 141 GB VRAM and 4800 GB/s bandwidth handle most inference tasks efficiently.

Startups or prototyping phases favor its lower 700W TDP and wider availability across four providers starting at $0.50 per hour.