B200 SXM vs RTX 2080 Ti

The B200 vastly outpaces the RTX 2080 Ti in raw compute: its FP16 capability stands at 4500 TFLOPS versus 10.1 TFLOPS, and FP8 hits 9000 TFLOPS where the competitor lacks specification. This disparity favors the B200 for AI training and inference, where lower precision formats like FP16 and FP8 accelerate matrix operations by up to 100 times over FP32 on older hardware. The B200's FP32 rate of 90 TFLOPS still exceeds the RTX 2080 Ti's 10.1 TFLOPS, but the real edge lies in specialized AI tensor cores. Memory differences transform workloads: 192 GB HBM3e at 8000 GB/s on the B200 supports batch sizes in the thousands for large language models, preventing out-of-memory errors common on the RTX 2080 Ti's 11 GB GDDR6 at 616 GB/s. Smaller batches on the RTX 2080 Ti limit throughput in training loops, often requiring model sharding. Power draw reflects this: B200's 1000W TDP enables sustained high performance in clusters, while the RTX 2080 Ti's 215W suits edge deployments but throttles under prolonged AI loads.

The B200 SXM excels in enterprise-scale AI deployments. Its 192 GB VRAM and 8000 GB/s bandwidth handle massive datasets for LLM training without fragmentation, ideal for organizations processing models exceeding 100 billion parameters. Cloud users benefit from NVLink, PCIe 6.0, and InfiniBand interconnects for multi-GPU scaling at $1.71 per hour starting price.

The RTX 2080 Ti fits budget-conscious prototyping and gaming. At $0.06 per hour, it delivers 10.1 TFLOPS FP16 for small-scale inference or Stable Diffusion on 11 GB VRAM, sufficient for hobbyists or quick tests. Its PCIe form factor and 215W TDP integrate easily into desktops without datacenter infrastructure.