B200 vs RTX 5080

The B200's FP16 performance reaches 4500 TFLOPS, enabling rapid AI model training that the RTX 5080's 56.3 TFLOPS cannot match; this 80-fold gap accelerates large-scale deep learning iterations. FP32 performance shows less disparity at 90 TFLOPS for B200 versus 56.3 TFLOPS for RTX 5080, but B200's FP8 at 9000 TFLOPS optimizes inference for quantized models. In training, B200's 192 GB VRAM supports batch sizes impossible on RTX 5080's 16 GB, reducing overhead in transformer models.

Memory bandwidth defines real-world throughput: B200's 8000 GB/s sustains data flows for massive datasets, allowing larger batches and fewer swaps compared to RTX 5080's 960 GB/s. This benefits inference latency in production, where B200 handles enterprise loads via InfiniBand interconnects. Power efficiency tilts toward RTX 5080 at 360W TDP for edge deployments, but B200's SXM and NVL form factors scale clusters efficiently despite 1000W draw.

Choose the B200 for large-scale LLM training or scientific simulations requiring over 192 GB VRAM. Its 8000 GB/s bandwidth and 4500 TFLOPS FP16 enable processing billion-parameter models without memory constraints, ideal for research labs or cloud providers. Multi-GPU setups via NVLink make it superior for distributed workloads at $1.71 per hour starting price.

Opt for the RTX 5080 in cost-sensitive gaming, content creation, or small-scale inference with 16 GB VRAM sufficiency. Its 360W TDP and $0.25 per hour pricing suit desktops or lightweight cloud instances. Developers fine-tuning compact models benefit from 56.3 TFLOPS FP16 without datacenter overhead.