B200 SXM vs RTX 5080

The B200 SXM dominates in AI-specific compute: its 4500 TFLOPS FP16 throughput accelerates model training and inference far beyond the RTX 5080's 56.3 TFLOPS, allowing 80 times faster low-precision operations. The B200 SXM's FP32 performance of 90 TFLOPS also surpasses the RTX 5080's 56.3 TFLOPS for tasks requiring full precision, such as simulations. This FP16-to-FP32 imbalance on B200 SXM optimizes it for deep learning pipelines, where mixed precision cuts training time without accuracy loss.

Memory specs reshape workload feasibility. B200 SXM's 192 GB HBM3e VRAM supports batch sizes for billion-parameter LLMs that exceed RTX 5080's 16 GB limit, preventing out-of-memory errors in training. Its 8000 GB/s bandwidth sustains data flow for large models, compared to 960 GB/s on RTX 5080, which bottlenecks high-throughput inference and reduces effective batch sizes by over eightfold.

Power and form factor influence deployment. B200 SXM's 1000W TDP powers dense clusters via NVLink, ideal for scaled training, while RTX 5080's 360W suits energy-efficient single-GPU inference or graphics.

The B200 SXM stands out for large-scale LLM training: 192 GB VRAM accommodates models over 100 billion parameters, and 4500 TFLOPS FP16 halves iteration times versus consumer GPUs. Its 8000 GB/s bandwidth and NVLink interconnect enable multi-GPU setups for distributed training at scales unattainable on PCIe-only cards.

High-volume inference benefits from B200 SXM's 9000 TFLOPS FP8 performance and InfiniBand support, delivering server-grade throughput for production deployments.

The RTX 5080 fits cost-sensitive prototyping: at $0.25 per hour average $0.38, its 56.3 TFLOPS FP16 handles fine-tuning of models under 7 billion parameters within 16 GB VRAM limits. Lower 360W TDP reduces operational costs for intermittent cloud use.

Graphics-intensive tasks like Stable Diffusion thrive on RTX 5080's balanced FP32 at 56.3 TFLOPS and GDDR7 efficiency, offering accessible performance without datacenter premiums.