GTX 1080 vs RTX 3060

The RTX 3060 outperforms the GTX 1080 in raw compute with 12.7 TFLOPS FP16 and FP32 versus 8.9 TFLOPS, translating to approximately 43 percent higher throughput for machine learning operations. This delta accelerates training cycles and inference latency in deep learning frameworks. For training large models, the FP16 advantage enables mixed-precision workflows at higher speeds on the RTX 3060. Inference benefits similarly, as higher FP32 sustains longer sequences without bottlenecks. The GTX 1080's equal FP16 and FP32 at 8.9 TFLOPS suits balanced legacy tasks but lags in modern tensor core utilization absent in Pascal. Memory differences prove critical: RTX 3060's 12 GB GDDR6 and 360 GB/s bandwidth support larger batch sizes than GTX 1080's 8 to 11 GB GDDR5X at 320 GB/s. Higher bandwidth reduces data transfer stalls during gradient computations, allowing batch sizes up to 20 percent larger on RTX 3060 for equivalent models. Power efficiency favors RTX 3060 slightly with 170W TDP against 180W, minimizing cloud costs over extended runs. These specs position Ampere for scalable AI pipelines.

The GTX 1080 suits scenarios requiring Pascal-specific optimizations or legacy software compatibility not fully ported to Ampere. Its 8 to 11 GB GDDR5X VRAM handles moderate models where 8.9 TFLOPS FP32 suffices for non-time-critical inference. With pricing from $0.30 per hour, it appeals if RTX 3060 availability is limited, as only 2 live offers exist versus 12 for the competitor.

The RTX 3060 excels in most contemporary workloads due to 12.7 TFLOPS FP16 performance and 12 GB VRAM, enabling larger models and batches than GTX 1080's 8.9 TFLOPS and 8 to 11 GB. Its 360 GB/s bandwidth outperforms 320 GB/s for data-intensive tasks. At $0.03 per hour average $0.07, it delivers superior value across abundant cloud offers.