L40 vs RTX 3080 Ti

Spec differences translate directly to real-world workloads. The L40's 90.5 TFLOPS in FP16 and FP32 dwarfs the RTX 3080 Ti's 29.8 TFLOPS, accelerating matrix multiplications central to neural network training and inference by approximately three times. This FP16 and FP32 parity in both GPUs suits mixed-precision training, but the L40's superior throughput reduces epoch times significantly. VRAM disparity proves critical: 48 GB on the L40 accommodates massive batch sizes or models like 70B parameter LLMs intact, while the RTX 3080 Ti's 10 to 12 GB forces smaller batches or model sharding, increasing overhead. Memory bandwidth edges higher at 864 GB/s for the L40 over 760 GB/s, minimizing bottlenecks in data-heavy operations and enabling larger effective batch sizes without accuracy loss. Power efficiency favors the L40 slightly with 300W TDP versus 320W, yielding better performance per watt. Both use PCIe form factors without specified interconnects, limiting multi-GPU scaling equally.

Opt for the NVIDIA L40 in scenarios demanding high VRAM and compute density, such as training large language models exceeding 12 GB or running inference on high-resolution Stable Diffusion variants. Its 48 GB GDDR6 and 90.5 TFLOPS FP32 handle enterprise-scale fine-tuning without fragmentation, ideal for data centers prioritizing throughput over cost. Cloud users facing memory constraints find the L40 essential at $0.67 per hour starting price.

Select the NVIDIA GeForce RTX 3080 Ti for budget-sensitive prototyping or lightweight inference where 10 to 12 GB GDDR6X suffices, such as small-scale fine-tuning or basic Stable Diffusion at $0.08 per hour. Its 29.8 TFLOPS FP16 supports entry-level AI tasks efficiently, appealing to hobbyists or short experiments valuing low average $0.14 hourly costs over capacity.