GH200 vs L40S

The GH200's FP16 performance of 1979 TFLOPS vastly outpaces the L40S's 362 TFLOPS, enabling dramatically faster training of large language models where half-precision dominates. Conversely, L40S leads in FP32 at 91 TFLOPS against GH200's 67 TFLOPS, suiting scientific simulations or rendering that demand single-precision accuracy. FP8 capabilities further favor GH200 at 3958 TFLOPS over 724 TFLOPS, accelerating inference on quantized models.

Memory specifications define real-world viability: GH200's 96 GB HBM3 and 4000 GB/s bandwidth support enormous batch sizes in training, reducing epochs by handling datasets without frequent swaps. L40S's 48 GB GDDR6X at 864 GB/s limits it to smaller batches, potentially slowing workflows on memory-intensive tasks. This bandwidth gap translates to 4.6 times faster data throughput for GH200, critical for transformer models.

Power dynamics influence deployment: GH200's 900 W TDP demands robust cooling and infrastructure, while L40S's 350 W enables denser racks. Interconnects amplify this: NVLink-C2C on GH200 scales to superpods, minimizing latency versus L40S's PCIe 4.0 bottlenecks in clusters.

The GH200 excels in large-scale LLM training and fine-tuning where 96 GB HBM3 VRAM and 4000 GB/s bandwidth handle massive models without partitioning. Its 1979 TFLOPS FP16 and 3958 TFLOPS FP8 throughput accelerate convergence on datasets exceeding 48 GB, ideal for research labs scaling to trillions of parameters.

Enterprise HPC clusters benefit from NVLink-C2C interconnects, enabling seamless multi-GPU communication at PCIe 5.0 speeds despite the 900 W TDP.

The L40S suits cost-sensitive inference deployments with its $1.66 per hour pricing and 350 W TDP, fitting edge datacenters or mixed workloads. Superior 91 TFLOPS FP32 performance aids visualization and simulations, while 48 GB GDDR6X suffices for batch inference on models under 40 billion parameters.

PCIe form factor simplifies integration into existing servers, making it preferable for Stable Diffusion pipelines or fine-tuning smaller models where 362 TFLOPS FP16 proves ample.