B300 vs L40S

The B300's FP16 performance of 2250 TFLOPS vastly outpaces the L40S's 362 TFLOPS, enabling faster AI model training where mixed-precision computations dominate, potentially reducing training times by over sixfold for large datasets. FP32 rates are nearly identical at 90 TFLOPS for B300 and 91 TFLOPS for L40S, meaning traditional scientific simulations see minimal gains from upgrading. FP8 capabilities at 4500 TFLOPS on B300 versus 724 TFLOPS on L40S accelerate inference for quantized large language models.

Massive 288 GB HBM3e VRAM on the B300 supports enormous batch sizes and multi-billion parameter models without swapping, unlike the L40S's 48 GB GDDR6X which limits scale. The 12000 GB/s bandwidth of B300 ensures rapid data movement critical for memory-bound tasks, contrasting the L40S's 864 GB/s that bottlenecks large-batch training. Higher 1200W TDP on B300 demands robust cooling, while L40S's 350W suits denser deployments.

NVLink and NVSwitch on B300 enable multi-GPU scaling beyond L40S's PCIe 4.0, ideal for distributed training clusters.

Opt for the B300 in scenarios demanding extreme memory capacity, such as training models exceeding 100 billion parameters that require 288 GB HBM3e VRAM. Its 2250 TFLOPS FP16 and 12000 GB/s bandwidth excel in large-batch distributed training across NVLink-connected clusters. High-end cloud users prioritizing throughput over cost benefit from 4500 TFLOPS FP8 for inference at scale.

The L40S suits budget-conscious deployments with its $0.40 per hour starting price and 350W TDP for efficient PCIe-based servers. It handles mid-sized inference or fine-tuning tasks effectively with 362 TFLOPS FP16 and 91 TFLOPS FP32, matching B300 in single-precision workloads. Broader availability across 18 cloud offers makes it ideal for prototyping or smaller-scale AI pipelines.