A30 vs H100 SXM5

The H100 SXM5 dominates in compute: its 1979 TFLOPS FP16 rate crushes the A30's 10.3 TFLOPS, accelerating deep learning training by orders of magnitude. FP32 performance shows 67 TFLOPS versus 10.3 TFLOPS, benefiting simulations and precise computations. FP8 at 3958 TFLOPS on H100 enables ultra-efficient inference for quantized models, absent on A30.

Memory specs reshape workloads profoundly: 3350 GB/s bandwidth on H100 SXM5 supports massive batch sizes in LLM training, reducing iterations compared to A30's 933 GB/s limit. The 80 to 94 GB HBM3 VRAM handles models exceeding 24 GB HBM2 threshold of A30, preventing out-of-memory errors in fine-tuning or diffusion tasks. Higher TDP of 700W on H100 versus 165W on A30 demands robust cooling, but yields throughput gains for large-scale deployments.

In real-world terms, A30 suits smaller inference runs: H100 SXM5 transforms training timelines from days to hours for billion-parameter models.

The A30 excels in power-constrained environments: its 165W TDP fits edge servers or clusters avoiding high electricity costs, unlike H100 SXM5's 700W draw. PCIe form factor simplifies integration into existing systems without SXM5 infrastructure.

Budget-conscious inference for models under 24 GB VRAM favors A30: lower upfront and operational costs persist despite no current live cloud offers, ideal for prototyping or legacy Ampere-optimized code.

The H100 SXM5 shines for large-scale AI training: 1979 TFLOPS FP16 and 80 to 94 GB VRAM manage massive LLMs, far beyond A30's 10.3 TFLOPS and 24 GB limits.

High-throughput inference benefits from 3358 GB/s bandwidth and FP8 support at 3958 TFLOPS: it processes bigger batches efficiently. Cloud availability from $0.80 per hour across 32 offers suits scalable projects with NVLink and InfiniBand interconnects.