L40S vs A100

Performance gaps between the L40S and A100 center on precision formats critical for AI. The L40S delivers 362 TFLOPS in FP16 and 91 TFLOPS in FP32, surpassing the A100's 312 TFLOPS FP16 and 19.5 TFLOPS FP32: this favors L40S for FP32-dominant tasks like scientific simulations, while FP16 edges aid mixed-precision training.

Memory bandwidth reveals a stark divide: the A100's 2039 GB/s HBM2e dwarfs the L40S's 864 GB/s GDDR6X, enabling larger batch sizes in training and inference for models like LLMs. Higher bandwidth reduces data bottlenecks, allowing the A100 to process bigger datasets without stalling compute units.

FP8 capability on the L40S at 724 TFLOPS accelerates quantized inference, cutting latency for deployment. Power draw differs at 350W for L40S versus 400W for A100, impacting density in clusters. Interconnects favor A100 with NVLink alongside PCIe 4.0, boosting multi-GPU scaling over L40S's PCIe 4.0 alone.

Opt for the L40S in workloads demanding high FP32 throughput: its 91 TFLOPS crushes the A100's 19.5 TFLOPS for graphics rendering or simulations. The 2023 Ada Lovelace architecture with 724 TFLOPS FP8 suits modern quantized inference, and 48 GB GDDR6X handles diverse models efficiently at 350W TDP.

PCIe form factor simplifies single-node deployments without NVLink complexity, ideal for cost-conscious users despite $1.65 per hour starting price.

Choose the A100 for bandwidth-intensive AI training: 2039 GB/s supports massive batch sizes versus L40S's 864 GB/s, accelerating LLM convergence. NVLink and InfiniBand enable superior multi-GPU scaling over PCIe-only L40S.

Abundant supply at $0.13 per hour from 34 offers makes it economical for large-scale deployments, with up to 80 GB HBM2e VRAM fitting enormous models.