H200 vs L4

The H200 dominates in raw compute: its FP16 performance hits 1979 TFLOPS and FP32 reaches 67 TFLOPS, far exceeding the L4's 121 TFLOPS FP16 and 30.3 TFLOPS FP32. This gap translates to faster LLM training on the H200, where FP32 precision handles optimization steps, and FP16 accelerates forward passes for models exceeding the L4's 24 GB VRAM limit. For inference, the H200's FP8 at 3958 TFLOPS versus 242 TFLOPS enables higher throughput on quantized large language models. Memory bandwidth disparity proves critical: 4800 GB/s on the H200 supports massive batch sizes without bottlenecks, ideal for training sequences over 100k tokens, while the L4's 300 GB/s constrains it to smaller batches around 24 GB capacity. Power draw reflects this: 700W TDP for H200 demands robust cooling, but 72W on L4 enables dense deployments.

Choose the H200 for large-scale LLM training or inference requiring over 24 GB VRAM. Its 141 GB HBM3e handles models like 175B parameter GPT variants without splitting, and 4800 GB/s bandwidth sustains batch sizes up to 10x larger than L4 equivalents. Datacenter users with NVLink or InfiniBand interconnects benefit from multi-GPU scaling at $0.49/hr starting price.

Opt for the L4 in cost-sensitive inference for models under 24 GB VRAM. Its 72W TDP fits edge servers or dense racks, and PCIe 4.0 simplifies integration versus H200's SXM/NVL forms. At $0.32/hr average $0.78/hr, it delivers 121 TFLOPS FP16 economically for real-time applications like recommendation systems.