L40S vs RTX 4090

The L40S outperforms in FP16 at 362 TFLOPS versus the RTX 4090's 165 TFLOPS, accelerating mixed-precision training where models leverage half-precision for speed: this delta enables faster convergence on large neural networks. Its FP32 rate of 91 TFLOPS edges out 82.6 TFLOPS, benefiting single-precision scientific simulations. The doubled 48 GB VRAM on the L40S sustains larger batch sizes in LLM training, reducing overhead from model swapping compared to the 24 GB limit on the RTX 4090.

Memory bandwidth favors the RTX 4090 at 1008 GB/s over 864 GB/s, improving throughput in bandwidth-bound inference scenarios like high-resolution image generation. For FP8 inference optimized for deployment, the L40S's 724 TFLOPS surpasses 660 TFLOPS, supporting quantized models at scale. Lower TDP of 350W on the L40S versus 450W aids dense cloud packing, though real-world efficiency hinges on workload memory intensity.

The L40S excels in enterprise deployments requiring 48 GB VRAM: large-scale LLM training or fine-tuning of models exceeding 24 GB fits perfectly, avoiding fragmentation. Its superior FP16 at 362 TFLOPS and FP32 at 91 TFLOPS handle compute-intensive tasks efficiently despite the $1.66 per hour average cost.

The RTX 4090 suits budget-conscious users with its $0.39 per hour average pricing across 75 offers: workloads like Stable Diffusion or smaller inference batches thrive on 24 GB VRAM and 1008 GB/s bandwidth. Higher availability makes it ideal for prototyping or high-volume parallel jobs where 165 TFLOPS FP16 suffices.