H200 NVL vs RTX 5060

H200's FP16 performance reaches 1979 TFLOPS, far exceeding its 67 TFLOPS FP32, which optimizes AI training and inference leveraging low-precision formats for speed. RTX 5060 balances at 23.1 TFLOPS for both FP16 and FP32, suiting graphics rendering where FP32 precision matters more evenly. This delta means H200 accelerates deep learning pipelines by handling mixed-precision computations efficiently, while RTX 5060 performs adequately for general compute but lags in high-throughput AI. Memory bandwidth of 4800 GB/s on H200 supports enormous batch sizes in model training, enabling faster iterations on datasets without bottlenecks. RTX 5060's 448 GB/s limits it to smaller batches, slowing large-scale operations. VRAM capacity underscores this: 141 GB on H200 fits billion-parameter models entirely, versus 12 GB on RTX 5060 requiring model sharding or quantization.

H200 excels in enterprise scenarios demanding massive scale. Large language model training benefits from 141 GB VRAM and NVLink interconnects for multi-GPU clusters. Cloud deployments at $0.50 per hour suit bursty workloads without upfront hardware costs. High bandwidth of 4800 GB/s ensures sustained performance in inference serving for production environments.

RTX 5060 fits consumer and developer setups prioritizing affordability and simplicity. Gaming and Stable Diffusion generation leverage its 23.1 TFLOPS FP32 at 180W TDP for desktop efficiency. Local fine-tuning of small models under 12 GB VRAM avoids cloud pricing. PCIe form factor integrates easily into personal workstations.