B200 vs L40

The B200's FP16 performance of 4500 TFLOPS dwarfs the L40's 90.5 TFLOPS, accelerating AI training and inference where half-precision computations dominate. This delta means training large language models completes over 49 times faster on the B200, assuming linear scaling. FP32 rates align closely at 90 TFLOPS for the B200 and 90.5 TFLOPS for the L40, suiting traditional scientific simulations equally.

Memory bandwidth profoundly impacts real-world usage: the B200's 8000 GB/s supports massive batch sizes for stable training of models exceeding 48 GB VRAM, preventing out-of-memory errors common on the L40. Lower 864 GB/s on the L40 limits it to smaller batches, increasing iteration times in memory-bound tasks like diffusion models.

FP8 capability at 9000 TFLOPS on the B200 optimizes inference for quantized LLMs, reducing latency versus the L40's lack of specified FP8 support. The B200's 1000W TDP demands robust cooling, while the L40's 300W fits standard PCIe setups, influencing deployment scalability.

The B200 excels in scenarios requiring extreme scale, such as training LLMs with billions of parameters that demand 192 GB HBM3e VRAM. Its 4500 TFLOPS FP16 and 8000 GB/s bandwidth enable large batch sizes, cutting training time significantly compared to the L40's constraints.

High-throughput inference benefits from the B200's 9000 TFLOPS FP8, ideal for serving massive models in production environments where the L40's 48 GB VRAM falls short.

The L40 suits cost-sensitive deployments with its pricing from $0.67 per hour, averaging $0.89 per hour, making it viable for prototyping or smaller-scale AI tasks. Its 300W TDP integrates easily into PCIe systems without specialized power infrastructure.

Workloads like fine-tuning mid-sized models or general visualization leverage the L40's 90.5 TFLOPS FP16/FP32 balance, where the B200's higher cost and power draw provide diminishing returns.