B200 SXM vs GTX 1080 Ti

B200 SXM's FP16 performance of 4500 TFLOPS vastly exceeds GTX 1080 Ti's 8.9 TFLOPS: training deep neural networks proceeds over 500 times faster on B200 SXM, reducing epochs from days to minutes for large datasets. The FP32 rating of 90 TFLOPS on B200 SXM compared to 8.9 TFLOPS supports precise simulations in scientific computing without precision loss.

Memory bandwidth defines practical limits: 8000 GB/s on B200 SXM permits batch sizes exceeding 1000 in inference for large language models, minimizing latency, whereas 320 GB/s on GTX 1080 Ti restricts batches to under 32, causing frequent data swaps and slowdowns. VRAM of 192 GB versus 11 GB allows B200 SXM to load 100 billion parameter models entirely, enabling full-precision inference unavailable on GTX 1080 Ti.

FP8 capability at 9000 TFLOPS on B200 SXM accelerates quantized inference for deployment, a feature absent on Pascal-era GTX 1080 Ti.

Select NVIDIA B200 SXM for large-scale LLM training: 192 GB VRAM accommodates models over 70 billion parameters, and 4500 TFLOPS FP16 cuts training time dramatically. High interconnects like NVLink and PCIe 6.0 scale multi-GPU clusters efficiently for distributed workloads.

Inference at production volumes favors B200 SXM: 9000 TFLOPS FP8 and 8000 GB/s bandwidth deliver thousands of tokens per second with large batches.

NVIDIA GeForce GTX 1080 Ti fits budget prototyping of small models: $0.60 per hour pricing and 180W TDP minimize costs and power draw for individual developers. Its PCIe form factor integrates easily into standard cloud instances for quick tests.

Light inference or gaming emulation suits GTX 1080 Ti: 8.9 TFLOPS FP32 handles sub-1 billion parameter models or Stable Diffusion at low resolutions without overkill expenses.