GH200 vs L4

The GH200's FP16 throughput of 1979 TFLOPS towers over the L4's 121 TFLOPS, enabling dramatically faster deep learning training where half-precision computations dominate. In inference scenarios, FP8 performance follows suit at 3958 TFLOPS for GH200 versus 242 TFLOPS for L4, accelerating quantized model serving. FP32 capabilities show GH200 at 67 TFLOPS against L4's 30.3 TFLOPS, benefiting simulation-heavy tasks. Memory bandwidth disparity proves critical: GH200's 4000 GB/s supports batch sizes far larger than L4's 300 GB/s limit, minimizing data loading bottlenecks in training large language models. This allows GH200 to process datasets with less fragmentation. Power draw highlights trade-offs, with GH200's 900W TDP demanding robust cooling versus L4's efficient 72W, influencing cloud instance density.

The GH200 excels in large-scale LLM training and fine-tuning where 96 GB HBM3 VRAM accommodates models exceeding 24 GB, preventing out-of-memory errors common on L4. Its 1979 TFLOPS FP16 performance cuts training epochs significantly, ideal for research labs or enterprises handling trillion-parameter models. High interconnects like NVLink-C2C and PCIe 5.0 enable multi-GPU scaling unavailable on L4's PCIe 4.0.

The L4 suits cost-sensitive inference deployments with pricing from $0.32/hr and 72W TDP allowing dense server packing. Its 24 GB GDDR6 handles standard vision or lightweight NLP inference without GH200's $1.99/hr overhead. PCIe form factor simplifies integration in varied cloud setups.