A100 SXM4 40GB vs H100 SXM5

The H100 demonstrates superior compute capabilities over the A100, particularly in FP16 at 1979 TFLOPS compared to 312 TFLOPS, which accelerates deep learning training and inference for models leveraging half-precision arithmetic. FP32 performance reaches 67 TFLOPS on the H100 versus 19.5 TFLOPS on the A100, benefiting general-purpose floating-point tasks in scientific simulations. The addition of FP8 at 3958 TFLOPS on the H100 optimizes low-precision inference for large language models, reducing latency in deployment scenarios.

Memory specifications further widen the gap: H100's 3350 GB/s bandwidth and 80 GB HBM3 VRAM support larger batch sizes and bigger models than the A100's 2039 GB/s and 40 GB HBM2e, minimizing data transfer bottlenecks during training. This enables handling of models exceeding 40 GB without excessive swapping. Higher TDP of 700W on the H100 demands robust cooling but delivers proportional gains in throughput for memory-intensive workloads.

Interconnect advancements include PCIe 5.0 and enhanced NVLink on the H100 versus PCIe 4.0 on the A100, improving multi-GPU scaling in clusters.

The A100 SXM4 40GB suits cost-sensitive deployments where workloads fit within 40 GB VRAM and do not require peak Hopper performance. At an average cloud price of $2.63 per hour from $1.00 per hour across five offers, it provides strong value for legacy Ampere-optimized applications or environments with 400W power constraints. Smaller-scale fine-tuning or inference tasks benefit from its 312 TFLOPS FP16 without the H100's higher 700W TDP overhead.

Opt for the H100 SXM5 when tackling cutting-edge AI workloads demanding high throughput, such as training massive LLMs. Its 1979 TFLOPS FP16 and 80 GB HBM3 VRAM handle larger models and batch sizes efficiently, with 3350 GB/s bandwidth reducing bottlenecks. Despite averaging $3.54 per hour from $0.80 per hour over 32 offers, the performance uplift justifies the cost for production-scale inference leveraging 3958 TFLOPS FP8.