B300 SXM6 vs H200 NVL

The B300 SXM6 delivers superior compute with 2250 TFLOPS in FP16 and 90 TFLOPS in FP32, exceeding the H200 NVL's 1979 TFLOPS FP16 and 67 TFLOPS FP32. This delta accelerates deep learning training, where FP32 precision handles gradient computations, enabling faster convergence on large models. FP8 performance at 4500 TFLOPS on the B300 outpaces 3958 TFLOPS on the H200, optimizing inference for quantized models in deployment scenarios.

Memory specifications define real-world usability: 288 GB HBM3e VRAM on the B300 supports batch sizes up to double those on the H200's 141 GB, reducing out-of-memory errors in LLM training. Bandwidth of 12000 GB/s versus 4800 GB/s minimizes data transfer bottlenecks, allowing sustained high throughput for memory-intensive tasks like fine-tuning. Higher TDP of 1200W on the B300 demands robust cooling, but yields proportional performance uplift over the H200's 700W.

Interconnects further differentiate them: NVSwitch and NVLink on the B300 enable seamless multi-GPU scaling, while the H200 NVL supports NVLink, PCIe 5.0, and InfiniBand for versatile clustering.

The B300 SXM6 excels in scenarios requiring extreme memory capacity, such as training LLMs exceeding 141 GB VRAM. Its 288 GB HBM3e and 12000 GB/s bandwidth handle massive batch sizes without swapping, ideal for research labs developing frontier models. Despite $2.45 per hour starting price, the 2250 TFLOPS FP16 justifies selection for time-critical projects.

The H200 NVL suits cost-conscious deployments with its $0.50 per hour entry price and 1979 TFLOPS FP16 performance. Lower 700W TDP reduces operational costs in power-sensitive clouds, while 141 GB VRAM suffices for most fine-tuning or inference tasks. NVLink and InfiniBand provide flexible interconnects for varied cluster setups.