B200 SXM vs L40

The B200's compute prowess dominates AI accelerators. It achieves 4500 TFLOPS in FP16, enabling rapid training of large neural networks, while L40 manages only 90.5 TFLOPS in FP16. FP32 performance aligns closely at 90 TFLOPS for B200 and 90.5 TFLOPS for L40, but B200's 9000 TFLOPS FP8 excels in inference, reducing latency for quantized models in production.

Memory architecture shapes practical limits. B200's 192 GB HBM3e VRAM and 8000 GB/s bandwidth accommodate enormous batch sizes and multi-billion parameter models without fragmentation. L40's 48 GB GDDR6 and 864 GB/s constrain it to modest scales, often necessitating techniques like gradient checkpointing that extend training durations.

TDP varies significantly: B200 requires 1000W, suiting specialized clusters with NVLink, while L40's 300W PCIe form factor supports dense, power-efficient inference farms. These traits favor B200 for throughput-critical paths and L40 for balanced operational costs.

Select the B200 SXM for workloads demanding extreme scale. Its 192 GB HBM3e VRAM handles models beyond L40's 48 GB capacity, critical for training foundation models. The 4500 TFLOPS FP16 accelerates iterations on vast datasets.

Enterprise AI platforms benefit from B200's $1.71 per hour starting rate when NVLink interconnects enable multi-GPU training at 8000 GB/s bandwidth, justifying the investment for production-grade performance.

The L40 suits budget-conscious deployments. At $0.67 per hour average $0.89 per hour, it delivers 90.5 TFLOPS FP16 for inference on models fitting 48 GB VRAM.

Its 300W TDP and PCIe form factor enable high-density servers for prototyping or serving smaller LLMs, where 864 GB/s bandwidth meets needs without excessive infrastructure costs.