B200 NVL vs L40

The B200 NVL's FP16 performance of 4500 TFLOPS vastly exceeds the L40's 90.5 TFLOPS, making it superior for training large neural networks that rely on mixed-precision computations to speed up iterations while maintaining accuracy. In contrast, FP32 performance remains comparable at 90 TFLOPS for the B200 NVL and 90.5 TFLOPS for the L40, suiting traditional single-precision workloads equally. The B200 NVL's FP8 capability at 9000 TFLOPS optimizes inference for quantized models, reducing latency in deployment scenarios. Higher memory bandwidth of 8000 GB/s on the B200 NVL versus 864 GB/s on the L40 enables larger batch sizes, which shortens training times and improves throughput for memory-bound tasks like transformer models. The B200 NVL's 1000W TDP demands robust cooling and power infrastructure, unlike the L40's efficient 300W, influencing deployment in dense cloud clusters.

Opt for the NVIDIA B200 NVL in scenarios requiring massive VRAM, such as training LLMs with billions of parameters that exceed 48 GB, leveraging its 192 GB HBM3e to avoid fragmentation. Its 4500 TFLOPS FP16 and 8000 GB/s bandwidth excel in multi-GPU setups via NVLink and PCIe 6.0, ideal for research labs or enterprises pushing model scales. The form factors SXM and NVL support high-density racks for exascale computing.

Select the NVIDIA L40 for budget-conscious deployments where 48 GB GDDR6 suffices, such as fine-tuning mid-sized models or running multiple inference instances, with pricing from $0.67 per hour across 14 providers. Its 300W TDP fits standard PCIe slots and lower-power environments, enabling scalable clusters without specialized infrastructure. Balanced FP16 and FP32 at 90.5 TFLOPS handles graphics and simulation tasks efficiently.