B300 vs H100

The B300's FP16 throughput of 2250 TFLOPS exceeds the H100's 1979 TFLOPS by 14 percent, accelerating deep learning training where half-precision computations dominate. FP32 performance follows suit at 90 TFLOPS for the B300 versus 67 TFLOPS for the H100, benefiting simulations and certain inference pipelines. In real-world training of large language models, this delta shortens epochs and scales to bigger datasets.

FP8 capabilities shine for inference: the B300 delivers 4500 TFLOPS against the H100's 3958 TFLOPS, supporting quantized models at higher speeds. The B300's 288 GB VRAM dwarfs the H100's 80-94 GB, allowing batch sizes up to three times larger without out-of-memory errors. Memory bandwidth of 12000 GB/s on the B300 versus 3350 GB/s on the H100 minimizes stalls, enabling sustained utilization above 90 percent in transformer workloads.

Power draw reflects these gains: the B300's 1200W TDP doubles the H100's 700W, demanding robust cooling but yielding efficiency per watt in high-throughput scenarios.

The B300 suits deployments requiring massive VRAM, such as training LLMs exceeding 100 billion parameters on the 288 GB HBM3e without multi-node sharding. Its 12000 GB/s bandwidth and 2250 TFLOPS FP16 performance excel in hyperscale inference serving thousands of users simultaneously. Cloud users prioritize it for future-proofing against models growing beyond H100's 80-94 GB limits, despite the $2.45 per hour starting price.

The H100 fits cost-conscious projects leveraging its mature ecosystem and wide availability across 55 cloud offers starting at $0.80 per hour. Workloads like fine-tuning mid-sized models under 70 billion parameters thrive on its 1979 TFLOPS FP16 and 3350 GB/s bandwidth without overprovisioning power at 700W TDP. It remains ideal for PCIe or NVL form factors in mixed-use clusters.