H200 vs L40S

The H200's FP16 throughput of 1979 TFLOPS vastly exceeds the L40S's 362 TFLOPS, accelerating deep learning training where half-precision dominates. This gap translates to faster convergence in large neural networks, potentially reducing training time by over 5x for FP16-heavy workloads. Conversely, the L40S edges out in FP32 at 91 TFLOPS against the H200's 67 TFLOPS, benefiting simulations or rendering that rely on single-precision compute.

Memory specifications define real-world usability: the H200's 141 GB HBM3e supports batch sizes up to 3x larger than the L40S's 48 GB GDDR6X, minimizing out-of-memory errors in LLM inference. The 4800 GB/s bandwidth on the H200 ensures data flows 5.5x faster, sustaining high utilization during token generation or gradient updates.

Power and form factors impact deployment: the H200's 700W TDP demands robust cooling versus the L40S's efficient 350W, but SXM and NVLink enable denser clusters for exascale performance.

Select the H200 for workloads requiring extreme memory capacity, such as training LLMs with billions of parameters. Its 141 GB HBM3e VRAM handles models that exceed the L40S's 48 GB limit, while 4800 GB/s bandwidth supports large batch sizes without slowdowns. Cloud pricing from $0.49 per hour makes it viable for bursty high-scale jobs across 9 providers.

Multi-GPU setups favor the H200 due to NVLink and PCIe 5.0, outperforming the L40S's PCIe 4.0 in distributed training.

The L40S excels in power-constrained or budget-limited environments with its 350W TDP and average $1.66 per hour pricing. It suffices for FP32-dominant tasks at 91 TFLOPS, such as scientific visualizations, where the H200's 67 TFLOPS FP32 offers no advantage.

Inference on mid-sized models benefits from the L40S's 362 TFLOPS FP16 and PCIe form factor, enabling easy integration without specialized infrastructure.