GTX 1080 Ti vs L40

The L40 outperforms the GTX 1080 Ti dramatically in compute: 90.5 TFLOPS versus 8.9 TFLOPS in FP16 and FP32 represents over ten times the throughput. This delta accelerates AI training and inference significantly; for instance, training a model requiring FP32 precision completes roughly ten times faster on the L40. Since both GPUs match FP16 and FP32 rates, the L40 leverages Ada Lovelace tensor cores more efficiently for mixed-precision workflows common in deep learning.

Memory specs further favor the L40: 48 GB GDDR6 VRAM supports batch sizes up to six times larger than the GTX 1080 Ti's 8-11 GB, reducing out-of-memory errors in large language models. The 864 GB/s bandwidth, 2.7 times the GTX 1080 Ti's 320 GB/s, minimizes data transfer bottlenecks during inference, enabling higher throughput for real-time applications. In practice, these translate to the L40 handling modern datasets infeasible on the older Pascal chip.

The GTX 1080 Ti suits budget-conscious users for lightweight inference on small models fitting within 8-11 GB VRAM. At $0.60 per hour, it undercuts the L40's $0.67 starting price for tasks like basic computer vision where 8.9 TFLOPS suffices and 180W TDP aligns with low-power cloud instances. Legacy Pascal-optimized codebases also favor it over newer architectures.

Opt for the L40 in demanding AI workloads needing 48 GB VRAM for large models or 90.5 TFLOPS for rapid training cycles. Its 864 GB/s bandwidth handles high-batch inference efficiently, ideal for production-scale LLM serving. Despite higher average pricing at $0.89 per hour, the performance justifies it for throughput-sensitive applications.