GTX 1080 vs L40S

The L40S vastly outperforms the GTX 1080 in compute capabilities: its 362 TFLOPS FP16 is over 40 times higher than the GTX 1080's 8.9 TFLOPS, enabling dramatically faster model training where half-precision arithmetic dominates. For FP32 tasks, the L40S's 91 TFLOPS provides a 10-fold increase, benefiting scientific simulations and general compute. The FP16 to FP32 ratio on the GTX 1080 remains 1:1 at 8.9 TFLOPS each, limiting efficiency in mixed-precision training common today.

Memory differences profoundly impact workloads: the L40S's 48 GB VRAM supports batch sizes impossible on the GTX 1080's 8 to 11 GB, such as loading large language models without swapping. Its 864 GB/s bandwidth, nearly three times the GTX 1080's 320 GB/s, reduces bottlenecks in data-heavy inference and training. The L40S's FP8 support at 724 TFLOPS further accelerates inference for quantized models.

Higher TDP of 350 W on the L40S versus 180 W reflects its datacenter design for sustained loads, while both use PCIe form factors, with the L40S adding PCIe 4.0 interconnect.

The GTX 1080 suits budget-constrained users for light gaming emulation or basic inference on small models fitting within 8 to 11 GB VRAM. At $0.30 per hour average $0.45 per hour, it offers low-cost entry for hobbyists or prototyping non-AI tasks like legacy scientific computing where 8.9 TFLOPS FP32 suffices. Compatibility with older software stacks favors it over newer GPUs in constrained environments.

The L40S excels in professional AI pipelines requiring 48 GB VRAM for large models and 864 GB/s bandwidth for high-throughput training. Its 362 TFLOPS FP16 and 91 TFLOPS FP32 deliver enterprise-grade performance for LLM development, despite $1.10 per hour average pricing. Datacenter users prioritize it for scalable inference with FP8 at 724 TFLOPS.