H200 NVL vs L40S

The H200's 141 GB HBM3e VRAM enables handling of models far larger than the L40S's 48 GB GDDR6X limit, allowing full loading of massive LLMs without model parallelism. Memory bandwidth of 4800 GB/s, compared to 864 GB/s, accelerates data transfers: this supports larger batch sizes in training, reducing iterations and time-to-convergence. In FP16 operations central to deep learning training, the H200 delivers 1979 TFLOPS versus 362 TFLOPS, a 5.5-fold gain that shortens epochs for large datasets. FP8 performance at 3958 TFLOPS versus 724 TFLOPS enhances quantized inference throughput. The L40S edges FP32 at 91 TFLOPS over 67 TFLOPS, benefiting simulations or rendering where single-precision dominates. H200's 700W TDP contrasts with L40S's 350W, influencing power efficiency in dense clusters: lower TDP permits more L40S units per rack.

Select the H200 for workloads demanding extreme memory capacity, such as training LLMs exceeding 48 GB total size. Its 141 GB HBM3e VRAM and 4800 GB/s bandwidth minimize sharding overhead in multi-GPU setups via NVLink. H200 NVL pricing from $0.50 per hour suits enterprises prioritizing scale over density.

The L40S fits budget-conscious deployments for inference or fine-tuning models under 48 GB. PCIe form factor and 350W TDP enable easy integration into standard servers, with FP32 at 91 TFLOPS aiding graphics-heavy tasks. At $0.40 per hour average $1.17, it offers value across 21 cloud offers.