GB300 SXM6 vs GTX 1080 Ti

Raw compute power sets the GPUs apart dramatically: the GB300 SXM6 delivers 2250 TFLOPS FP16 and 90 TFLOPS FP32, compared to 11.3 TFLOPS for both on the GTX 1080 Ti. The GB300's FP16 dominance, over 25 times higher than FP32, optimizes deep learning training and inference with mixed precision, speeding matrix multiplications in transformers by factors beyond the 1080 Ti's balanced but dated profile.

Memory systems amplify this: 12000 GB/s bandwidth and 288 GB VRAM on the GB300 support batch sizes for billion-parameter models, preventing out-of-memory errors common on the 1080 Ti's 484 GB/s and 11 GB limits. Large models like GPT variants require such capacity for efficient training; the 1080 Ti suits only sub-10 GB inference.

Efficiency differs too: the GB300's 1400W TDP enables NVSwitch scaling for clusters, while the 1080 Ti's 250W fits edge or desktop use without interconnects.

The GB300 SXM6 dominates large-scale AI workloads: its 288 GB HBM3e VRAM loads trillion-parameter LLMs for training, and 4500 TFLOPS FP8 accelerates inference at scales impossible on 11 GB hardware. Datacenter operators choose it for NVLink clusters handling 12000 GB/s data flows in production environments.

High-performance computing demands its 2250 TFLOPS FP16 over the GTX 1080 Ti's constraints.

The GTX 1080 Ti fits low-budget, light-duty tasks: at $0.60 per hour, it runs Stable Diffusion or small-model inference within 11 GB VRAM and 484 GB/s bandwidth. Its 250W TDP and PCIe form factor enable easy desktop or small cloud instances without datacenter infrastructure.

Hobbyists or legacy app testing prefer its accessibility over the GB300's unpriced, power-hungry profile.