B300 SXM6 vs GTX 1080

The B300's FP16 performance of 2250 TFLOPS vastly outpaces the GTX 1080's 8.9 TFLOPS, enabling faster AI training and inference where half-precision computations dominate. Its FP32 rate of 90 TFLOPS still exceeds the GTX 1080's 8.9 TFLOPS, but the real advantage lies in FP8 at 4500 TFLOPS for ultra-efficient inference on quantized models. The GTX 1080's equal FP16 and FP32 rates suit general-purpose tasks from its era, yet cannot handle modern deep learning demands. Memory capacity defines usability: the B300's 288 GB HBM3e supports enormous batch sizes and massive models, while the GTX 1080's 8 to 11 GB GDDR5X limits it to small datasets or models under 8 GB. Bandwidth reinforces this: 12000 GB/s on the B300 accelerates data transfers for large-scale training, permitting bigger batches without bottlenecks, whereas 320 GB/s on the GTX 1080 constrains throughput. Power draw underscores scale: 1200 W TDP for the B300 in SXM form with NVSwitch and NVLink for multi-GPU clusters, versus 180 W PCIe on the GTX 1080 with no interconnect.

The B300 SXM6 excels in demanding AI workloads requiring vast resources. Large language model training benefits from 288 GB VRAM and 2250 TFLOPS FP16, handling models far beyond the GTX 1080's 8 GB limit. High-throughput inference leverages 4500 TFLOPS FP8 and 12000 GB/s bandwidth for production deployments.

The GTX 1080 suits budget-conscious users with light compute needs. Prototyping small neural networks or inference on models under 8 GB VRAM works within its 8.9 TFLOPS FP16 and $0.30 per hour pricing. Low 180 W TDP fits constrained environments where 1200 W and $2.45 per hour prove excessive.