H200 NVL vs L4

The FP16 dense throughput reaches 989 TFLOPS on every H200 variant including the NVL model and 121 TFLOPS on every L4 variant. This eightfold gap in dense FP16 performance means training runs that rely on dense matrix operations complete substantially faster on the H200 NVL than on the L4. When sparsity is enabled the FP16 figure with sparsity reaches 1979 TFLOPS on the H200 family and 242 TFLOPS on the L4 family so the same ratio holds for sparse workloads. Memory bandwidth at 4800 GB/s on the H200 family versus 300 GB/s on the L4 family directly influences maximum batch size because higher bandwidth sustains larger data transfers without stalling the tensor cores. FP32 performance stands at 67 TFLOPS for the H200 family and 30.3 TFLOPS for the L4 family which further widens the advantage for precision sensitive scientific kernels on the H200 NVL.

The H200 NVL suits workloads that require the 141 GB memory capacity to hold entire large language models during training or fine tuning. Its NVLink bridge supports low latency scaling across multiple cards when batch sizes exceed what a single 24 GB device can accommodate. The 4800 GB/s bandwidth shared by all H200 variants further enables these large batches without memory bottlenecks.

The L4 fits inference or fine tuning tasks where the 72 W TDP keeps total system power low and where the 24 GB GDDR6 memory suffices for models under that limit. Its PCIe form factor integrates easily into existing servers without NVLink infrastructure. The 300 GB/s bandwidth shared by all L4 variants remains adequate for moderate batch sizes in these lighter scenarios.