GB300 SXM6 vs H200 SXM

Compute performance favors the GB300 in precision-critical tasks. FP16 throughput of 2250 TFLOPS exceeds H200's 1979 TFLOPS by 14 percent, accelerating mixed-precision training for large neural networks. FP32 at 90 TFLOPS versus 67 TFLOPS supports 34 percent faster single-precision simulations or inference pipelines. FP8's 4500 TFLOPS on GB300 outpaces H200's 3958 TFLOPS, ideal for quantized inference on trillion-parameter models.

Memory specifications transform real-world workloads. The GB300's 288 GB VRAM and 12000 GB/s bandwidth permit batch sizes up to twice those on H200's 141 GB and 4800 GB/s, reducing training iterations and latency for long-context LLMs. Higher bandwidth minimizes bottlenecks in data-heavy operations like transformer attention layers.

Power efficiency varies by scenario. H200's 700W TDP suits dense clusters with cooling limits, consuming half the power of GB300's 1400W. Yet GB300 delivers superior flops per watt in memory-bound tasks, enhancing throughput for sustained AI training runs.

Opt for the NVIDIA GB300 SXM6 in scenarios demanding extreme memory capacity. Its 288 GB HBM3e VRAM handles models exceeding 1 trillion parameters or datasets too large for H200's 141 GB limit. The 12000 GB/s bandwidth supports massive batch sizes in LLM training, cutting time-to-convergence.

Future deployments favor GB300 for Blackwell Ultra's 2250 TFLOPS FP16 performance, preparing infrastructure for 2025 AI advancements.

Select the NVIDIA H200 SXM for immediate availability and cost efficiency. Live pricing from $1.19 per hour across 21 offers enables quick scaling, unlike GB300's lack of offers. Its 700W TDP fits power-constrained environments, halving consumption versus GB300's 1400W.

H200 suffices for Hopper-era workloads with 1979 TFLOPS FP16, balancing performance and 4800 GB/s bandwidth for production inference.