B200 NVL vs GTX 1080

B200's FP16 performance of 4500 TFLOPS vastly outpaces GTX 1080's 8.9 TFLOPS, accelerating AI training where half-precision reduces memory demands and speeds iterations. Its FP32 capability of 90 TFLOPS exceeds GTX 1080's 8.9 TFLOPS, aiding precise simulations in scientific computing. The pronounced FP16/FP32 delta on B200 signals specialization for mixed-precision workflows: training leverages FP16 for throughput, inference taps FP8 at 9000 TFLOPS. GTX 1080's balanced 8.9 TFLOPS across precisions suits uniform legacy loads but falters on scale. Memory bandwidth defines real-world limits: B200's 8000 GB/s supports massive batch sizes in deep learning, enabling efficient data flow for large models. GTX 1080's 320 GB/s constrains batches, often requiring model sharding or reduced sizes with its 8-11 GB VRAM versus B200's 192 GB. This yields 25x bandwidth advantage, slashing training times for VRAM-bound tasks.

Opt for B200 NVL in high-throughput AI scenarios like LLM training on trillion-parameter models, where 192 GB HBM3e VRAM and 4500 TFLOPS FP16 enable full-batch processing without offloading. Its 8000 GB/s bandwidth and NVLink interconnect scale across nodes at $10.50 per hour, ideal for enterprise inference with FP8 at 9000 TFLOPS. Datacenter demands with 1000W TDP justify selection over consumer alternatives.

Choose GTX 1080 for budget prototyping or gaming at $0.30 per hour, where 8.9 TFLOPS FP32 suffices for light Stable Diffusion or small-model inference. Its 180W TDP and PCIe form factor fit low-power edge setups or legacy CUDA code without NVLink needs. Cost savings shine in non-scale-sensitive tasks limited by 8-11 GB VRAM.