L40S vs P100

The L40S outperforms P100 dramatically in compute: 362 TFLOPS FP16 versus 9.3 TFLOPS enables up to 39 times faster mixed-precision training for deep learning models. Its FP32 at 91 TFLOPS doubles effective throughput for single-precision scientific simulations compared to P100's 9.3 TFLOPS. FP8 at 724 TFLOPS on L40S accelerates inference for quantized large language models, a capability absent in P100.

Memory differences impact real-world usage profoundly: L40S's 48 GB VRAM supports batch sizes three times larger than P100's 16 GB, reducing overhead in training large models. The 864 GB/s bandwidth versus 732 GB/s sustains higher throughput during data-intensive operations like Stable Diffusion generation. In inference, L40S handles concurrent requests efficiently due to superior FP16 ratios.

Power efficiency varies: L40S at 350W delivers 1.03 TFLOPS per watt in FP16, outperforming P100's 0.037 TFLOPS per watt at 250W. PCIe 4.0 on L40S provides modern scalability, while P100's NVLink excels in legacy multi-node HPC clusters.

The L40S excels in contemporary AI workloads requiring high VRAM and compute. For LLM training or inference, its 48 GB GDDR6X and 362 TFLOPS FP16 handle models up to billions of parameters without swapping, unlike P100's 16 GB limit. Cloud availability across 18 offers starting at $0.40 per hour makes it viable for scalable deployments.

Users prioritizing Ada Lovelace features like FP8 at 724 TFLOPS choose L40S for efficient quantized inference and Stable Diffusion tasks.

The P100 suits legacy applications locked to Pascal architecture, such as older scientific computing codes optimized for 9.3 TFLOPS FP32 and NVLink interconnects. Its single cloud offer at $0.60 per hour offers predictability for low-volume, compatibility-driven runs where recoding is impractical.

Budget-conscious users with small batch sizes under 16 GB VRAM select P100 for basic FP16 tasks at 250W TDP, avoiding L40S's higher average $1.10 per hour cost.