H100 PCIe vs L40

The H100 PCIe dominates in FP16 performance at 1979 TFLOPS, over 21 times the L40's 90.5 TFLOPS, accelerating neural network training where half-precision computations prevail. For FP32, the L40 matches its FP16 at 90.5 TFLOPS, surpassing the H100's 67 TFLOPS, which suits traditional scientific simulations but lags in mixed-precision AI training. This FP16 to FP32 delta means the H100 excels in large-scale model training, while the L40 offers balanced throughput for inference or FP32-heavy tasks. Memory bandwidth profoundly impacts real-world usage: the H100's 3350 GB/s supports massive batch sizes in transformer models, reducing iteration times, whereas the L40's 864 GB/s limits scalability for datasets exceeding 48 GB VRAM. Power draw differs markedly at 700W TDP for H100 versus 300W for L40, influencing cooling and density in cloud instances. Overall, H100 prioritizes raw throughput for exascale AI, L40 favors efficiency.

Select the NVIDIA H100 PCIe for LLM training or fine-tuning where VRAM exceeds 48 GB, as its 80 to 94 GB HBM3 handles models like GPT-scale without partitioning. The 1979 TFLOPS FP16 and 3350 GB/s bandwidth enable 10x faster iterations on large batches compared to L40's constraints. High TDP of 700W suits dedicated clusters despite $1.25 to $2.73 per hour pricing.

Opt for the NVIDIA L40 in cost-sensitive inference or Stable Diffusion pipelines, where 90.5 TFLOPS FP16/FP32 suffices and 48 GB GDDR6 manages most production loads. Its 300W TDP cuts power costs by more than half versus H100's 700W, with pricing from $0.67 averaging $0.89 per hour. Balanced architecture fits graphics-accelerated computing without overprovisioning.