L40S vs Tesla V100 16GB

The L40S dominates in mixed-precision workloads due to its FP16 rating of 362 TFLOPS, nearly tripling the V100's 125 TFLOPS; this accelerates deep learning training and inference where half-precision is standard. FP32 performance shows an even larger gap at 91 TFLOPS for the L40S versus 15.7 TFLOPS for the V100, benefiting scientific simulations or legacy code requiring full precision.

VRAM capacity is the key differentiator: 48 GB on the L40S supports larger batch sizes and complex models that exceed the V100's 16 GB limit, reducing the need for model parallelism. Bandwidth is similar with 864 GB/s versus 900 GB/s, so the V100 holds a slight edge in memory-intensive tasks fitting within its constraints, but the L40S's extra capacity mitigates bottlenecks for real-world AI scaling.

Power draw is close at 350W TDP for the L40S and 300W for the V100, implying comparable efficiency in dense deployments. Overall, the L40S translates specs to 2-6x speedups in modern frameworks like PyTorch for transformer-based models.

Opt for the L40S in scenarios demanding high VRAM and throughput, such as training large language models exceeding 16 GB or running FP8 inference at 724 TFLOPS. Its 48 GB GDDR6X and Ada Lovelace features excel in multi-GPU setups via PCIe 4.0, ideal for cloud users prioritizing speed over cost.

The L40S suits fine-tuning or generative AI where FP16 performance of 362 TFLOPS halves training times compared to the V100.

Choose the V100 16GB for cost-sensitive legacy applications or small-scale inference fitting within 16 GB HBM2. At $0.10 per hour starting price, it offers value for prototyping or workloads leveraging NVLink in older clusters.

It remains viable for FP32-heavy scientific computing at 15.7 TFLOPS where the V100's 900 GB/s bandwidth supports high-throughput data movement without needing the L40S's power overhead.