H100 SXM5 vs L40

The H100 SXM5 dominates in FP16 performance at 1979 TFLOPS, enabling rapid matrix multiplications essential for deep learning training. The L40 matches its FP16 and FP32 at 90.5 TFLOPS each, providing balanced compute for inference where FP32 precision matters more. This FP16 to FP32 delta means the H100 accelerates training phases by factors tied to its 21x FP16 advantage, while L40 handles inference or simulations without such disparity.

Memory specifications profoundly impact workloads: the H100's 3350 GB/s bandwidth and 80 to 94 GB HBM3 VRAM support massive batch sizes in large language model training, reducing iterations needed. The L40's 864 GB/s and 48 GB GDDR6 limit it to smaller batches, potentially slowing convergence in memory-bound tasks. Higher bandwidth on H100 minimizes data transfer bottlenecks during gradient computations.

Power consumption differs markedly with H100 at 700W TDP versus L40's 300W. This makes H100 suitable for dense clusters but raises cooling demands, while L40 offers density advantages in cost-sensitive deployments.

Select the H100 SXM5 for large-scale LLM training or fine-tuning where FP16 performance of 1979 TFLOPS and 80 to 94 GB HBM3 VRAM handle models exceeding 48 GB. Its 3350 GB/s bandwidth sustains high throughput in multi-GPU setups via NVLink and PCIe 5.0. Scenarios demanding FP8 at 3958 TFLOPS, such as optimized inference on massive models, favor this GPU despite 700W TDP.

High-performance computing tasks benefit from Hopper's tensor core focus over Ada Lovelace balance.

Opt for the L40 in cost-sensitive inference or graphics workloads, leveraging its $0.67 per hour starting price and 300W TDP for efficient scaling. Equal FP16 and FP32 at 90.5 TFLOPS suit Stable Diffusion generation or visualization without H100's overhead. PCIe form factor simplifies integration in diverse cloud instances.

Batch inference on models fitting within 48 GB GDDR6 benefits from Ada Lovelace optimizations at lower average $0.89 per hour cost.