L40 vs RTX 4080

The L40's 48 GB GDDR6 VRAM dwarfs the RTX 4080's 16 GB GDDR6X, enabling larger batch sizes and model sizes in training and inference: for instance, LLMs exceeding 16 GB fit natively on the L40. This memory advantage directly impacts throughput in deep learning pipelines.

Memory bandwidth tells a similar story: the L40's 864 GB/s outpaces the RTX 4080's 717 GB/s, minimizing data transfer bottlenecks during high-volume operations like gradient computations. FP16 and FP32 performance at 90.5 TFLOPS on the L40 nearly doubles the RTX 4080's 48.7 TFLOPS, accelerating matrix multiplications central to training and inference by up to two times.

Power efficiency favors the L40 slightly with a 300W TDP against 320W, allowing sustained performance in dense cloud deployments without excessive heat. These specs translate to faster epochs in training and higher queries per second in inference for memory-bound workloads.

The L40 stands out for large-scale LLM training and fine-tuning where models demand over 16 GB VRAM: its 48 GB capacity supports batch sizes that the RTX 4080 cannot handle, reducing training time significantly. Datacenter tasks like scientific simulations or professional rendering leverage the 90.5 TFLOPS FP32 performance and 864 GB/s bandwidth for optimal results.

Multi-GPU configurations benefit from the L40's efficiency at 300W TDP, making it ideal for enterprise cloud rentals despite the $0.67 per hour starting price.

The RTX 4080 fits cost-sensitive inference or Stable Diffusion generation on models under 16 GB VRAM, where its $0.11 per hour pricing delivers strong value at 48.7 TFLOPS FP16 performance. Lighter fine-tuning or prototyping benefits from quick setup without overprovisioning memory.

Users prioritizing affordability over capacity select the RTX 4080 for gaming-adjacent ML tasks or small-scale scientific computing in cloud environments.