H100 NVL vs L40

The H100 NVL dominates in raw compute power, particularly for AI training where FP16 performance matters most: its 1979 TFLOPS vastly outpaces the L40's 90.5 TFLOPS, enabling faster model convergence on large datasets. For inference, the H100's FP8 capability at 3958 TFLOPS accelerates quantized models, reducing latency far beyond the L40's FP16 limits. The FP32 scores show nuance, with H100 at 67 TFLOPS trailing L40's 90.5 TFLOPS, but modern AI rarely bottlenecks on FP32 alone. Memory specs amplify real-world impacts: H100's 80 to 94 GB HBM3 VRAM supports massive batch sizes for training billion-parameter models, while 48 GB GDDR6 on L40 suits smaller or batched inference. Bandwidth disparity proves critical, as H100's 3350 GB/s handles data movement for large models without stalling, compared to L40's 864 GB/s which constrains throughput in memory-bound scenarios. Power draw underscores trade-offs, with H100's 700W TDP demanding robust cooling versus L40's efficient 300W.

Opt for the H100 NVL in scenarios requiring extreme scale, such as training large language models exceeding 48 GB VRAM or leveraging NVLink for multi-GPU clusters. Its 1979 TFLOPS FP16 and 3350 GB/s bandwidth excel in high-batch training, while FP8 at 3958 TFLOPS optimizes inference on massive models. Datacenter setups with InfiniBand or PCIe 5.0 interconnects benefit from its SXM5 and NVL form factors for peak HPC performance.

Choose the L40 for budget-conscious deployments focused on inference or visualization, where its 90.5 TFLOPS FP16 and FP32 balance suffices without H100's overhead. At 300W TDP and PCIe form factor, it fits dense, power-limited servers, and pricing from $0.67 per hour makes it ideal for scaling many instances. Workloads like Stable Diffusion or fine-tuning smaller models leverage its 48 GB GDDR6 efficiently.