B200 SXM vs GTX 1080

Raw compute reveals dominance by the B200: its 4500 TFLOPS FP16 vastly outpaces the GTX 1080's 8.9 TFLOPS, enabling faster AI training where half-precision dominates. The FP32 parity at 8.9 TFLOPS on the GTX 1080 suits general graphics, but B200's 90 TFLOPS FP32 still triples it for mixed workloads. This FP16-to-FP32 ratio on B200, 50:1, optimizes deep learning pipelines over the GTX 1080's balanced 1:1 profile.

Memory specs dictate real-world viability: B200's 192 GB HBM3e supports enormous batch sizes in model training, where GTX 1080's 8 to 11 GB GDDR5X limits to small datasets or low-resolution inference. Bandwidth at 8000 GB/s on B200 accelerates data movement for large language models, versus 320 GB/s on GTX 1080, which bottlenecks high-throughput tasks. Inference benefits from B200's 9000 TFLOPS FP8, slashing latency on quantized models impossible at scale on the older card.

Power and interconnects further the divide: B200's 1000W TDP and NVLink, PCIe 6.0, InfiniBand enable multi-GPU clusters, while GTX 1080's 180W PCIe form factor suits single-node desktops.

The B200 SXM excels in demanding AI and HPC environments: its 192 GB VRAM handles massive datasets for LLM training, where GTX 1080's 8 GB fails. Users prioritizing 4500 TFLOPS FP16 and 8000 GB/s bandwidth choose it for production-scale inference at $1.71 per hour starting price.

Multi-node setups leverage NVLink and InfiniBand on B200, ideal for distributed computing unavailable on GTX 1080.

The GTX 1080 fits low-budget, light-duty applications: at $0.30 per hour, its 8.9 TFLOPS FP32 suffices for legacy gaming or basic rendering without B200's 1000W power demands. Small-scale prototyping benefits from 180W efficiency and PCIe simplicity.

Enthusiasts or hobbyists select it for tasks under 11 GB VRAM, avoiding B200's $4.60 per hour average cost.