A30 vs H100

The H100's FP16 performance of 1979 TFLOPS dwarfs the A30's 10.3 TFLOPS, providing approximately 192 times the throughput for deep learning training and inference tasks that leverage half-precision arithmetic. This delta translates to drastically reduced training times for large neural networks. In FP32, the H100 achieves 67 TFLOPS against 10.3 TFLOPS, offering over sixfold improvement for precision-sensitive simulations or graphics workloads.

Memory capacity and bandwidth profoundly impact real-world usage: the H100's 80-94 GB HBM3 supports massive batch sizes and models exceeding 24 GB, preventing out-of-memory errors common on the A30. Its 3350 GB/s bandwidth, more than 3.5 times the A30's 933 GB/s, minimizes data transfer bottlenecks during model loading or gradient computations. For inference, the H100's FP8 capability at 3958 TFLOPS enables ultra-low latency serving of quantized large language models, far surpassing the A30's capabilities.

The A30 suits budget-conscious deployments where power efficiency is paramount. With a TDP of 165W, it enables higher density in racks compared to the H100's 700W draw. It fits legacy Ampere-optimized applications or smaller-scale inference with 24 GB HBM2 VRAM handling models under that threshold. Although no live cloud offers exist currently, its PCIe form factor and NVLink interconnect support straightforward integration in existing infrastructure.

Opt for the H100 in demanding AI workloads requiring extreme scale. Its 80-94 GB HBM3 VRAM accommodates trillion-parameter models, while 3350 GB/s bandwidth sustains high throughput. The 1979 TFLOPS FP16 and 3958 TFLOPS FP8 excel in training and inference for modern LLMs. Available from $0.80 per hour across 57 cloud offers, it justifies the power cost with versatile form factors like SXM5, PCIe, and NVL, plus advanced interconnects including PCIe 5.0 and InfiniBand.