B300 SXM6 vs H100 PCIe

The B300's superior FP16 performance of 2250 TFLOPS over the H100's 1979 TFLOPS accelerates inference tasks, while its 90 TFLOPS FP32 exceeds the H100's 67 TFLOPS for faster model training. FP8 throughput reaches 4500 TFLOPS on the B300 versus 3958 TFLOPS on the H100, benefiting quantized inference in large language models. These deltas translate to reduced training times: for instance, FP32 gains support quicker convergence in scientific simulations. Memory capacity stands out: 288 GB HBM3e on the B300 permits batch sizes impossible on the H100's 80-94 GB HBM3, avoiding out-of-memory errors in LLM fine-tuning. Bandwidth of 12000 GB/s on the B300 minimizes data transfer bottlenecks compared to 3350 GB/s on the H100, enabling larger effective batch sizes and higher throughput in memory-bound workloads. The B300's 1200W TDP demands more cooling than the H100's 700W, impacting deployment in power-constrained environments.

Opt for the NVIDIA B300 SXM6 in scenarios demanding extreme scale, such as training trillion-parameter LLMs where 288 GB HBM3e VRAM and 12000 GB/s bandwidth prevent memory limitations. Its 2250 TFLOPS FP16 and 4500 TFLOPS FP8 excel in inference for massive models, justifying $2.45 per hour starting pricing for future-proof investments. NVSwitch and NVLink interconnects enhance multi-GPU scaling in data centers.

Select the NVIDIA H100 PCIe for cost-effective deployments with mature software support, available from $1.25 per hour across 21 offers. Its 700W TDP suits edge or power-limited setups, while 1979 TFLOPS FP16 handles most current inference needs without excess capacity. PCIe 5.0 and InfiniBand provide flexible interconnects for varied clusters.