GB300 vs H100

The GB300's FP16 throughput of 2250 TFLOPS exceeds the H100's 1979 TFLOPS by 14 percent, speeding up training phases in deep learning where mixed-precision computations dominate. FP32 performance at 90 TFLOPS on the GB300 surpasses the H100's 67 TFLOPS, benefiting simulations and graphics rendering that rely on single-precision arithmetic. FP8 capabilities reach 4500 TFLOPS on the GB300 versus 3958 TFLOPS on the H100, optimizing inference latency for quantized models.

VRAM capacity defines a key divide: the GB300's 288 GB supports batch sizes three to four times larger than the H100's 80-94 GB limit, reducing communication overhead in distributed training. Memory bandwidth of 12000 GB/s on the GB300, triple the H100's 3350 GB/s, accelerates gradient updates and attention mechanisms in transformers, preventing bottlenecks in large-scale inference.

Higher TDP of 1400W on the GB300 versus 700W on the H100 implies greater peak output but demands advanced cooling, influencing deployment in dense clusters.

The GB300 excels in scenarios demanding massive scale, such as training models over 500 billion parameters: its 288 GB HBM3e VRAM avoids sharding across multiple GPUs. Bandwidth at 12000 GB/s sustains high-throughput operations in exascale AI research.

Enterprises planning 2025 data center refreshes select the GB300 for FP16 performance of 2250 TFLOPS and FP8 at 4500 TFLOPS, prioritizing future-proofing over current availability.

The H100 fits production environments needing instant deployment: cloud pricing starts at $0.80 per hour with an average of $3.19 per hour across 55 offers. Its 700W TDP lowers energy costs compared to the GB300's 1400W.

Versatile form factors like SXM5, PCIe, and NVL, plus interconnects including NVLink and PCIe 5.0, make the H100 suitable for hybrid on-premises and cloud setups without waiting for Blackwell availability.