B200 SXM vs RTX 2080

The B200's FP16 throughput of 4500 TFLOPS vastly outpaces the RTX 2080's 10.1 TFLOPS: this enables training massive neural networks in hours rather than days on the older card. FP32 performance shows a narrower 90 TFLOPS versus 10.1 TFLOPS gap, but the B200's tensor core optimizations favor mixed-precision training common in deep learning. Inference benefits similarly, with FP8 at 9000 TFLOPS on B200 accelerating low-precision deployments impossible at scale on RTX 2080.

Memory specs transform real-world usage: 192 GB HBM3e on B200 supports batch sizes for models exceeding 100 billion parameters, while 8-11 GB GDDR6 on RTX 2080 limits to small models or heavy quantization. Bandwidth of 8000 GB/s versus 616 GB/s reduces data bottlenecks, speeding iterations by orders of magnitude. TDP differences, 1000W for B200 and 215W for RTX 2080, reflect power scaling for datacenter clusters versus single-node efficiency.

Opt for the B200 in large-scale AI training or inference: its 192 GB VRAM handles trillion-parameter models, and 4500 TFLOPS FP16 throughput cuts training times dramatically. Datacenter environments with NVLink, PCIe 6.0, and InfiniBand interconnects maximize multi-GPU scaling unavailable on RTX 2080. Cloud deployments at $1.71 per hour justify costs for production workloads demanding peak performance.

Select the RTX 2080 for budget-conscious prototyping or gaming: at $0.05 per hour, it delivers 10.1 TFLOPS FP32 for lightweight inference on models under 7 billion parameters. Its 215W TDP and PCIe form factor suit edge devices or small-scale fine-tuning where 8-11 GB VRAM suffices. Legacy Turing compatibility aids quick tests without high power or interconnect needs.