B200 SXM vs RTX 5060

Compute capabilities define the core disparity: the B200 SXM achieves 4500 TFLOPS in FP16 and 9000 TFLOPS in FP8, enabling rapid training and inference for models with billions of parameters, whereas the RTX 5060's 23.1 TFLOPS FP16 limits it to smaller-scale tasks. The B200 SXM's FP32 at 90 TFLOPS supports scientific simulations requiring higher precision, unlike the RTX 5060's balanced 23.1 TFLOPS FP16 and FP32 suited for graphics rendering.

Memory specifications further separate their applications. The B200 SXM's 192 GB VRAM and 8000 GB/s bandwidth allow massive batch sizes in LLM training, minimizing per-iteration overhead. In comparison, the RTX 5060's 12 GB VRAM and 448 GB/s bandwidth constrain batch sizes, making it viable only for inference on compact models or fine-tuning with optimizations.

The B200 SXM dominates large-scale AI deployments: its 192 GB HBM3e VRAM fits models over 100 billion parameters, and 8000 GB/s bandwidth sustains high throughput. Multi-GPU clusters leverage NVLink, PCIe 6.0, and InfiniBand for distributed training at $1.71 per hour starting price.

Enterprise users prioritize its 4500 TFLOPS FP16 for production inference and HPC simulations demanding 90 TFLOPS FP32.

The RTX 5060 fits gaming, content creation, and small ML projects: 180W TDP supports standard desktop power supplies without datacenter cooling. Its 23.1 TFLOPS FP32 excels in real-time graphics and Stable Diffusion generation on 12 GB VRAM.

Developers prefer it for local prototyping where cloud costs like $4.60 per hour average for B200 SXM prove prohibitive.