H100 vs L40

The FP16 dense performance reaches 989 TFLOPS on the H100 compared to 181.05 TFLOPS on the L40. The FP32 performance measures 67 TFLOPS on the H100 against 90.5 TFLOPS on the L40. These values indicate that the H100 accelerates half precision operations far more effectively while the L40 holds an edge in single precision calculations. Memory bandwidth of 3350 GB per second on the H100 versus 864 GB per second on the L40 directly influences the feasible batch sizes during both training and inference passes. Larger batches become practical on the H100 because data movement keeps pace with the 989 TFLOPS FP16 dense throughput. The L40 bandwidth supports smaller batches suited to its 181.05 TFLOPS FP16 dense rating. When sparsity is enabled the H100 FP16 figure rises to 1979 TFLOPS while the L40 reaches 362.1 TFLOPS. The same pattern holds for FP8 dense where the H100 delivers 1979 TFLOPS against 362 TFLOPS on the L40. These ratios remain consistent across dense and sparsity modes and therefore favor the H100 for scale oriented workloads.

The H100 suits large scale LLM training because its 80 to 94 GB VRAM capacity accommodates models that exceed the 48 GB limit of the L40. Its 3350 GB per second bandwidth sustains the 989 TFLOPS FP16 dense rate during extended training runs that require frequent parameter updates. Workloads that rely on NVLink interconnect also select the H100 to maintain high speed multi GPU communication that the L40 PCIe 4.0 interface cannot match.

The L40 fits inference tasks that emphasize FP32 calculations at 90.5 TFLOPS where the H100 provides only 67 TFLOPS. Its 300 W TDP also aligns with deployments that operate under stricter power constraints than the 700 W TDP of the H100. Stable Diffusion workloads that remain within 48 GB VRAM benefit from the L40 when the higher FP32 rating accelerates certain rendering kernels.