B300 SXM6 vs L40S

The B300's FP16 performance of 2250 TFLOPS vastly outpaces the L40S's 362 TFLOPS, accelerating neural network training where mixed-precision computations dominate. This disparity means training epochs complete faster on the B300, especially for models exceeding 100 billion parameters that demand high throughput. FP32 figures are nearly identical at 90 TFLOPS versus 91 TFLOPS, so tasks like scientific simulations show minimal difference.

FP8 capabilities further favor the B300 at 4500 TFLOPS over 724 TFLOPS, optimizing inference for quantized large language models with reduced latency. Memory bandwidth of 12000 GB/s on the B300 supports larger batch sizes during training, preventing out-of-memory errors that constrain the L40S's 864 GB/s. The B300's 288 GB VRAM accommodates models up to hundreds of billions of parameters in a single GPU, while the L40S's 48 GB limits it to smaller batches or model parallelism.

Power consumption reflects these capabilities: the B300 draws 1200W in SXM form with NVSwitch and NVLink interconnects, enabling multi-GPU clusters, versus the L40S's 350W PCIe 4.0 setup suited for denser, lower-power racks.

The B300 excels in large-scale LLM training and inference where datasets and models surpass 48 GB VRAM. Its 288 GB HBM3e and 12000 GB/s bandwidth handle batch sizes that crash on the L40S, reducing training time via 2250 TFLOPS FP16. Data centers with NVLink infrastructure prioritize it for trillion-parameter models at $2.45 per hour starting price.

The L40S suits cost-sensitive deployments with moderate workloads, offering 91 TFLOPS FP32 and 362 TFLOPS FP16 at $0.40 per hour. Its 350W TDP and PCIe form factor enable high-density servers for fine-tuning or inference on models under 48 GB. Availability across 23 cloud offers makes it ideal for prototyping or edge AI without premium costs.