A30 vs H200

Memory capacity sets these GPUs apart fundamentally: A30's 24 GB HBM2 limits model sizes, while H200's 141 GB HBM3e supports large language models without quantization. Bandwidth follows suit, with H200's 4800 GB/s enabling larger batch sizes in training compared to A30's 933 GB/s, reducing data loading bottlenecks by over fivefold.

Compute performance favors H200 overwhelmingly. FP16 at 1979 TFLOPS on H200 accelerates inference and training versus A30's 10.3 TFLOPS. FP32 shows 67 TFLOPS for H200 against 10.3 TFLOPS, benefiting scientific simulations. H200's FP8 at 3958 TFLOPS further optimizes low-precision inference. For training, H200 handles bigger models and batches efficiently; inference benefits from high throughput on large inputs.

Power efficiency varies: A30's 165W TDP suits dense deployments, but H200's 700W delivers superior performance per watt in high-end scenarios due to architectural advances.

The A30 suits budget-conscious users with lighter workloads. Its 165W TDP enables deployment in power-sensitive environments like edge servers, where H200's 700W proves excessive. With 24 GB HBM2 and 10.3 TFLOPS FP16/FP32, it handles fine-tuning small models or basic inference without overkill.

No live cloud offers mean A30 fits on-premises setups or spot markets, avoiding H200's $0.50 to $3.62 per hour costs.

The H200 excels in demanding AI tasks requiring vast memory. Its 141 GB HBM3e VRAM loads full-scale LLMs, unlike A30's 24 GB limit. Bandwidth of 4800 GB/s supports massive batches, ideal for training.

High compute like 1979 TFLOPS FP16 and FP8 at 3958 TFLOPS make it optimal for production inference. Cloud availability from $0.50 per hour across 26 offers facilitates scalable deployments.