B300 vs H200

FP16 performance defines training efficiency: B300's 2250 TFLOPS exceeds H200's 1979 TFLOPS by 14 percent, speeding up large language model training where mixed precision dominates. FP32 throughput at 90 TFLOPS on B300 versus 67 TFLOPS on H200 supports scientific computing tasks reliant on single-precision arithmetic. FP8 figures of 4500 TFLOPS for B300 over 3958 TFLOPS for H200 enhance inference throughput for quantized models, reducing latency in deployment.

Memory specifications transform real-world usage. The B300's 288 GB HBM3e VRAM accommodates models and batches infeasible on H200's 141 GB, minimizing out-of-memory errors during fine-tuning or inference. Bandwidth of 12000 GB/s on B300 doubles H200's 4800 GB/s, enabling larger batch sizes that cut training time by improving GPU utilization. Higher TDP of 1200W on B300 demands robust cooling, unlike H200's 700W, but yields proportional gains in sustained workloads.

Interconnect options further differentiate: B300 relies on NVSwitch and NVLink for multi-GPU scaling, while H200 supports NVLink, PCIe 5.0, and InfiniBand, offering flexibility in varied cluster setups.

The B300 stands out for large-scale AI training where VRAM exceeds 141 GB, such as with massive LLMs requiring 288 GB capacity. Its 12000 GB/s bandwidth sustains high throughput during extended runs, reducing total training time compared to H200 limitations. Users prioritizing peak performance over cost select B300 despite $2.45 per hour starting price.

The H200 suits cost-sensitive inference and fine-tuning of models fitting within 141 GB VRAM, with FP8 at 3958 TFLOPS delivering strong throughput. Lower TDP of 700W lowers operational costs in power-constrained environments, and pricing from $0.50 per hour across 26 providers enhances accessibility. It excels where H200's Hopper architecture meets demands without B300's premium.