H100 vs L40

The H100 dominates in tensor-heavy AI tasks: its FP16 performance hits 1979 TFLOPS, over 21 times the L40's 90.5 TFLOPS, accelerating model training where half-precision dominates. For FP32 workloads, the L40 edges ahead at 90.5 TFLOPS versus H100's 67 TFLOPS, benefiting scientific simulations or graphics rendering that rely on single-precision floats. The H100's FP8 capability at 3958 TFLOPS further enhances low-precision inference, enabling quantized large language models at scale.

Memory bandwidth reveals stark differences: H100's 3350 GB/s supports larger batch sizes and faster data movement compared to L40's 864 GB/s, reducing bottlenecks in training loops with high-resolution datasets. VRAM capacity amplifies this: 80 to 94 GB on H100 accommodates models exceeding 48 GB on L40, preventing out-of-memory errors in fine-tuning or inference of massive transformers.

Power dynamics matter for deployments: H100's 700W TDP demands robust cooling versus L40's efficient 300W, influencing cloud instance density and operational costs.

Opt for the H100 in large-scale LLM training or fine-tuning: its 80 to 94 GB HBM3 VRAM handles models beyond 48 GB, while 1979 TFLOPS FP16 and 3350 GB/s bandwidth enable rapid iterations on datasets with billion-parameter architectures. Scenarios demanding NVLink interconnects or PCIe 5.0 for multi-GPU clusters favor H100, despite higher average pricing of $3.19 per hour.

Select the L40 for cost-sensitive inference or graphics workloads: at $0.67 per hour minimum and 300W TDP, it delivers 90.5 TFLOPS FP16 within 48 GB GDDR6 VRAM for Stable Diffusion or lighter LLMs. PCIe form factor simplifies single-node setups without H100's power overhead.