B200 SXM vs RTX 3080

The B200 SXM vastly outpaces the RTX 3080 in compute: 4500 TFLOPS FP16 versus 29.8 TFLOPS accelerates AI training by over 150 times in mixed-precision workflows, while FP32 at 90 TFLOPS edges out the RTX 3080's 29.8 TFLOPS for general simulation. FP8 capability of 9000 TFLOPS on B200 enables ultra-efficient inference for quantized LLMs. In real-world terms, this FP16 delta shortens training epochs from days to hours for large models. Memory defines feasibility: 192 GB HBM3e on B200 supports batch sizes exceeding millions of tokens without swapping, unlike the RTX 3080's 10-12 GB limit which caps at small batches and forces gradient accumulation. Bandwidth of 8000 GB/s versus 760 GB/s eliminates data starvation in inference pipelines, sustaining high throughput. TDP differs at 1000W for B200 versus 320W, but cloud infrastructure absorbs this without user concern.

Opt for the B200 SXM in large-scale AI deployments: its 192 GB VRAM accommodates full-parameter LLMs up to hundreds of billions, and 4500 TFLOPS FP16 completes training runs infeasible on consumer hardware. High-bandwidth interconnects like NVLink and PCIe 6.0 enable multi-GPU clusters for distributed workloads at $1.71 per hour entry pricing.

Select the RTX 3080 for cost-sensitive prototyping or gaming: at $0.06 per hour, its 29.8 TFLOPS FP16 handles fine-tuning of 7B models or Stable Diffusion with 10-12 GB VRAM sufficiency. Low 320W TDP suits edge deployments where scale is unnecessary.