L40S vs RTX 5080

The L40S demonstrates superior raw compute with 362 TFLOPS FP16 and 91 TFLOPS FP32, enabling faster matrix multiplications critical for deep learning training, which often relies on FP32 precision. The RTX 5080's balanced 56.3 TFLOPS across FP16 and FP32 suits general-purpose tasks but falls short by over 6 times in FP16, limiting its scalability for large-scale model training. The L40S's FP8 capability at 724 TFLOPS further accelerates inference on quantized models.

Memory configurations impact real-world usage profoundly: the L40S's 48 GB VRAM supports larger batch sizes in LLM training, accommodating models like 70B parameters without excessive swapping, while the RTX 5080's 16 GB restricts it to smaller batches or models under 13B parameters. Although the RTX 5080 provides higher bandwidth at 960 GB/s versus 864 GB/s, this advantage diminishes in VRAM-constrained scenarios, where the L40S handles memory-intensive inference more effectively. Power draw is similar, with 350W for L40S and 360W for RTX 5080.

Select the L40S for workloads demanding high VRAM, such as training or inferencing large language models exceeding 30B parameters, where its 48 GB capacity prevents out-of-memory errors. Datacenter reliability and PCIe 4.0 interconnect make it ideal for sustained enterprise deployments across multiple GPUs.

Opt for the RTX 5080 in budget-limited prototypes or inference on models under 13B parameters, leveraging its lower starting price of $0.25 per hour and Blackwell architecture efficiencies. Higher memory bandwidth at 960 GB/s benefits data-parallel tasks like Stable Diffusion with smaller datasets.