B200 vs L40

Dense FP16 throughput of 2250 TFLOPS on the B200 compared with 181.05 TFLOPS on the L40 translates to greater throughput for training loops and inference passes that depend on half precision arithmetic. FP16 with sparsity reaches 4500 TFLOPS on the B200 against 362.1 TFLOPS on the L40 preserving the same relative advantage when sparsity is applied. The memory bandwidth ratio of 8000 GB/s to 864 GB/s permits the B200 to sustain larger batch sizes by moving data at nine times the rate of the L40. FP8 dense performance of 4500 TFLOPS on the B200 versus 362 TFLOPS on the L40 extends the same pattern to lower precision inference. FP32 figures favor the L40 at 90.5 TFLOPS over the B200 at 75 TFLOPS indicating that certain scientific kernels may execute faster on the L40 despite lower tensor rates elsewhere. TDP values of 1000W and 300W further differentiate sustained operation limits.

The B200 suits large scale LLM training because its 192 GB VRAM capacity and 8000 GB/s bandwidth accommodate models that exceed 48 GB limits. Dense FP16 throughput of 2250 TFLOPS supports accelerated iteration on transformer based workloads that benefit from NVLink interconnect.

The L40 fits power constrained environments where 300W TDP matters and FP32 performance of 90.5 TFLOPS exceeds the 75 TFLOPS of the B200. PCIe form factor and 48 GB GDDR6 memory align with inference tasks that remain within moderate memory bounds.