B200 vs RTX 5060

The B200's FP16 performance of 4500 TFLOPS dwarfs the RTX 5060's 23.1 TFLOPS, enabling it to handle large-batch training and inference for models exceeding hundreds of billions of parameters. The RTX 5060's equal FP16 and FP32 at 23.1 TFLOPS suits smaller models or gaming, but lacks the B200's FP32 of 90 TFLOPS for precision-heavy scientific tasks. This FP16/FP32 delta means the B200 accelerates mixed-precision training by up to 50 times faster in real-world AI pipelines.

Memory bandwidth defines scalability: the B200's 8000 GB/s supports massive batch sizes for LLMs, reducing time-to-train for models like GPT-scale by allowing full context loading into 192 GB VRAM. The RTX 5060's 448 GB/s limits it to smaller batches, often requiring model sharding or quantization, which increases latency by 2-5x for inference on 70B+ parameter models. Power draw underscores efficiency: B200 at 1000W for datacenter density versus RTX 5060's 180W for edge or desktop use.

Interconnects amplify differences: B200's NVLink, PCIe 6.0, and InfiniBand enable multi-GPU clusters scaling to thousands, while RTX 5060's PCIe-only limits it to single-node tasks.

Select the B200 for enterprise AI training and inference on large language models requiring over 100 GB VRAM, such as full fine-tuning of 405B parameter models. Its 8000 GB/s bandwidth and 4500 TFLOPS FP16 ensure high throughput in multi-node clusters via NVLink and InfiniBand. Current pricing from $1.71 per hour justifies the investment for production-scale deployments across 16 cloud offers.

Opt for the RTX 5060 in cost-sensitive scenarios like prototyping small models under 13B parameters or gaming workloads, where 12 GB GDDR7 and 23.1 TFLOPS FP16 suffice at $0.07 per hour starting price. Its 180W TDP fits low-power edge inference or developer laptops. With 8 live offers averaging $0.14 per hour, it excels for quick experiments without datacenter overhead.