B200 SXM vs B300 SXM6

The B200 SXM excels in compute-intensive tasks due to its superior FP16 performance of 4500 TFLOPS compared to the B300 SXM6's 2250 TFLOPS: this advantage accelerates large language model training, where FP16 mixed precision reduces memory use while speeding iterations. Similarly, 9000 TFLOPS FP8 on the B200 doubles the B300's 4500 TFLOPS, enabling higher inference throughput for quantized models serving real-time queries.

Memory specs favor the B300: 288 GB VRAM versus 192 GB supports larger batch sizes in inference or fine-tuning of massive models, minimizing out-of-memory errors. Its 12000 GB/s bandwidth, 50 percent above the B200's 8000 GB/s, sustains data flow for memory-bound workloads, though FP32 parity at 90 TFLOPS equalizes traditional simulations. Higher 1200W TDP on B300 demands robust cooling, contrasting the B200's 1000W efficiency.

The B200 SXM fits compute-dominant scenarios like high-volume LLM inference: its 9000 TFLOPS FP8 outperforms the B300's 4500 TFLOPS, paired with lower $1.71 per hour starting pricing across 13 providers. Cost-sensitive training benefits from 4500 TFLOPS FP16 at half the B300's average $6.44 per hour rate, ideal when models fit within 192 GB VRAM.

The B300 SXM6 suits memory-constrained workloads such as fine-tuning enormous models: 288 GB HBM3e VRAM exceeds the B200's 192 GB, enabling larger contexts without sharding. Its 12000 GB/s bandwidth supports bigger batches in inference pipelines, justifying $2.45 per hour starting cost for data-center scale deployments.