L40 vs RTX 5080

The L40's 48 GB VRAM capacity significantly outpaces the RTX 5080's 16 GB, enabling larger batch sizes in training large language models or processing extensive datasets without swapping to system memory. This memory advantage proves critical for workloads exceeding 16 GB, such as fine-tuning models with billions of parameters. The L40's 90.5 TFLOPS in FP16 and FP32 delivers 60 percent higher throughput than the RTX 5080's 56.3 TFLOPS, accelerating both training iterations and inference queries.

Memory bandwidth impacts data transfer rates: the RTX 5080's 960 GB/s allows marginally faster movement of data compared to the L40's 864 GB/s, benefiting bandwidth-bound inference scenarios with smaller models. Power efficiency tilts toward the L40 at 300W TDP versus 360W, reducing operational costs in prolonged cloud sessions. The Blackwell architecture in the RTX 5080 may introduce optimizations absent in Ada Lovelace, potentially yielding better real-world efficiency despite lower peak TFLOPS.

For training, the FP16/FP32 parity at higher rates on the L40 supports mixed-precision workflows effectively. Inference benefits from ample VRAM on the L40 for concurrent requests, while the RTX 5080 suits latency-sensitive tasks with its bandwidth edge.

Opt for the L40 in memory-constrained environments like LLM training or fine-tuning where 48 GB VRAM handles models up to three times larger than the RTX 5080's 16 GB limit. Its 90.5 TFLOPS compute outperforms the 56.3 TFLOPS alternative, shortening job times despite higher $0.89 per hour average cost.

Select the RTX 5080 for cost-sensitive projects such as Stable Diffusion generation or lightweight inference, where $0.25 per hour starting price delivers value. The 960 GB/s bandwidth and Blackwell architecture suit smaller models under 16 GB VRAM, offering efficiency gains over the L40's 864 GB/s.