L40S vs RTX 4080 SUPER

Compute capabilities diverge sharply between the L40S and RTX 4080 SUPER. The L40S achieves 362 TFLOPS in FP16 for accelerated training and inference, paired with 91 TFLOPS FP32 for precise simulations, while the RTX 4080 SUPER matches only 48.7 TFLOPS in both formats. This FP16/FP32 delta means the L40S handles mixed-precision training 7.4 times faster in FP16, ideal for large neural networks.

Memory specifications further advantage the L40S: 48 GB VRAM supports models exceeding 16 GB limits of the RTX 4080 SUPER, preventing out-of-memory errors in LLM fine-tuning. The 864 GB/s bandwidth versus 717 GB/s allows larger batch sizes, reducing training epochs by enabling more data per iteration and cutting overall time.

Power draw reflects efficiency: L40S at 350W TDP sustains higher throughput, while RTX 4080 SUPER at 320W suits lower-density setups. In real-world terms, L40S excels in memory-bound tasks like diffusion models, where RTX 4080 SUPER suffices for smaller-scale inference.

Opt for the NVIDIA L40S in scenarios demanding extensive VRAM and compute, such as training large language models requiring over 16 GB. Its 48 GB GDDR6X handles massive datasets without splitting, and 362 TFLOPS FP16 accelerates convergence.

Datacenter deployments benefit from PCIe 4.0 and 864 GB/s bandwidth for high-throughput inference at scale, justifying $0.40/hr starting price across 20 offers.

Choose the NVIDIA GeForce RTX 4080 SUPER for budget-conscious tasks where 16 GB VRAM suffices, like lightweight inference or fine-tuning small models. At $0.17/hr average $0.32/hr, it delivers 48.7 TFLOPS FP16/FP32 cost-effectively.

Gaming-adjacent workloads or prototyping benefit from 320W TDP and 717 GB/s bandwidth, offering strong value across limited 3 cloud offers.