A30 vs H200 SXM

H200's FP16 performance of 1979 TFLOPS dwarfs A30's 10.3 TFLOPS: this accelerates mixed-precision training for deep learning models by nearly 192 times in raw throughput. FP32 sees H200 at 67 TFLOPS against A30's 10.3 TFLOPS, benefiting scientific simulations and precise inference. FP8 capability on H200 at 3958 TFLOPS enables ultra-efficient large model inference, absent on A30. Memory bandwidth of 4800 GB/s on H200 versus 933 GB/s on A30 supports larger batch sizes: H200 handles massive datasets without bottlenecks, ideal for training billion-parameter models. A30's 24 GB VRAM limits it to smaller batches or models under 20 GB effective size, while H200's 141 GB accommodates full precision LLMs up to 100 GB plus context. Power draw reflects this: H200's 700W TDP demands robust cooling, contrasting A30's efficient 165W.

NVIDIA A30 suits budget-constrained or low-power data centers: its 165W TDP consumes far less energy than H200's 700W. PCIe form factor integrates into standard servers without specialized infrastructure. It handles moderate inference or fine-tuning for models fitting within 24 GB HBM2 VRAM, especially where no live cloud offers exist for A30, favoring on-premises deployments.

NVIDIA H200 SXM dominates large-scale AI workloads: 141 GB HBM3e VRAM loads enormous models infeasible on A30's 24 GB. Current cloud pricing starts at $1.19 per hour across 23 offers, averaging $3.70 per hour. Superior interconnects like PCIe 5.0 and InfiniBand enable multi-GPU clusters for distributed training.