GTX 1080 vs P100

Memory specifications create the primary performance gap: P100's 16 GB HBM2 doubles GTX 1080's 8 to 11 GB GDDR5X, supporting larger batch sizes in training. P100's 732 GB/s bandwidth more than doubles GTX 1080's 320 GB/s, accelerating data transfers for memory-bound tasks like LLM inference. FP16 and FP32 rates remain close at 9.3 TFLOPS for P100 versus 8.9 TFLOPS for GTX 1080, implying similar speeds for single-precision training or half-precision inference on modest models. In real-world terms, P100 handles bigger models without out-of-memory errors, while GTX 1080 suits smaller datasets. NVLink on P100 enables efficient multi-GPU scaling for distributed training, unlike GTX 1080's PCIe limitation. Higher TDP of 250W on P100 versus 180W on GTX 1080 reflects greater compute headroom but increases power draw. Bandwidth advantage translates to 20 to 50 percent faster inference on large language models, depending on batch size.

Select GTX 1080 for power-sensitive single-GPU workloads with modest memory needs. Its 180W TDP consumes less energy than P100's 250W, ideal for edge deployments or cost-controlled clouds. At 8 to 11 GB VRAM and 320 GB/s bandwidth, it manages Stable Diffusion inference or fine-tuning small models efficiently. Pricing from $0.30 per hour suits sporadic gaming or graphics tasks where PCIe form factor fits consumer setups.

Choose P100 for memory-intensive or multi-GPU applications. 16 GB HBM2 VRAM and 732 GB/s bandwidth support larger batches in LLM training, outperforming GTX 1080's 8 to 11 GB GDDR5X. NVLink interconnect enables superior scaling across nodes, critical for scientific computing. Cloud pricing from $0.07 per hour average $0.25 per hour offers better value for sustained workloads.