L40S vs RTX 4080

The L40S outperforms RTX 4080 dramatically in compute metrics: 362 TFLOPS FP16 versus 48.7 TFLOPS enables up to 7.4 times faster tensor operations, critical for AI inference. FP32 performance at 91 TFLOPS on L40S exceeds RTX 4080's 48.7 TFLOPS by 87 percent, benefiting model training phases that rely on single-precision arithmetic.

Memory capacity defines workload feasibility: L40S's 48 GB VRAM supports models and batch sizes three times larger than RTX 4080's 16 GB limit, preventing out-of-memory errors in large language models. Higher bandwidth of 864 GB/s on L40S, compared to 717 GB/s, sustains these batches without bottlenecks, improving throughput by up to 20 percent in memory-bound tasks.

Power draw remains close with L40S at 350W TDP versus 320W, but L40S's FP8 capability at 724 TFLOPS accelerates quantized inference dramatically. These specs translate to L40S handling enterprise-scale training and high-concurrency inference, while RTX 4080 suits prototyping or smaller-scale deployments.

The L40S excels in scenarios demanding high VRAM and compute: training large language models benefits from 48 GB GDDR6X and 91 TFLOPS FP32, accommodating datasets that exceed RTX 4080's 16 GB capacity. Datacenter users prioritize its 362 TFLOPS FP16 and 724 TFLOPS FP8 for inference at scale, where PCIe 4.0 interconnect ensures reliable multi-GPU setups.

Enterprise inference pipelines favor L40S due to 864 GB/s bandwidth supporting massive batch sizes, even at $1.10 per hour average cost.

The RTX 4080 fits budget-conscious users: at $0.11 per hour starting price and $0.28 per hour average, it delivers solid 48.7 TFLOPS FP16 and FP32 for prototyping small models under 16 GB VRAM. Developers testing Stable Diffusion or fine-tuning compact networks find its 717 GB/s bandwidth and 320W TDP efficient for short bursts.

Cost-sensitive inference on modest scales prefers RTX 4080, avoiding L40S's higher $0.40 per hour entry point across fewer optimized offers.