B200 NVL vs RTX 3080

The B200's 4500 TFLOPS FP16 performance accelerates mixed-precision model training by a factor of 151 over the RTX 3080's 29.8 TFLOPS, enabling faster iterations on large datasets. Its FP32 capability of 90 TFLOPS triples the RTX 3080's 29.8 TFLOPS for tasks requiring full precision, such as scientific simulations. The FP16 to FP32 delta on B200 supports optimized training pipelines, while RTX 3080 maintains parity between formats suited to smaller-scale compute. For inference, B200's 9000 TFLOPS FP8 delivers ultra-low precision throughput ideal for serving massive models at scale; RTX 3080 lacks FP8 support, capping efficiency at FP16 levels. Memory specifications transform real-world usage: B200's 192 GB HBM3e versus 10-12 GB GDDR6X permits batch sizes orders of magnitude larger, reducing per-sample overhead in LLM training by accommodating models exceeding 100 billion parameters. The 8000 GB/s bandwidth on B200 eliminates data starvation in memory-intensive operations, compared to RTX 3080's 760 GB/s which constrains high-throughput scenarios. TDP differences of 1000W for B200 and 320W for RTX 3080 influence deployment density and power budgeting.

The B200 stands out for large-scale LLM training and inference where 192 GB VRAM and 4500 TFLOPS FP16 handle models beyond RTX 3080 capacity. Its 9000 TFLOPS FP8 optimizes high-volume serving, and NVLink interconnects enable multi-GPU clusters for distributed workloads. At $10.50 per hour, it suits production environments demanding speed over cost.

The RTX 3080 fits prototyping, Stable Diffusion generation, and fine-tuning of models under 7 billion parameters using its 10-12 GB VRAM and 29.8 TFLOPS FP16. Cloud pricing from $0.06 per hour averaging $0.13 makes it ideal for budget-conscious developers or gaming-integrated compute. Its 320W TDP supports dense personal or small-scale cloud setups.