GTX 1080 Ti vs RTX 5080

Compute capabilities differ profoundly between these GPUs: the RTX 5080's 56.3 TFLOPS in FP16 and FP32 surpasses the GTX 1080 Ti's 8.9 TFLOPS by a factor of 6.3 times, enabling significantly faster model training and inference tasks. In training scenarios, this delta translates to reduced epochs time; for instance, large language model training on the RTX 5080 completes iterations over six times quicker than on the GTX 1080 Ti, assuming equivalent batch sizes. Inference benefits similarly, with higher throughput for real-time applications. Memory bandwidth impacts workload scalability: the RTX 5080's 960 GB/s allows batch sizes up to three times larger than the GTX 1080 Ti's 320 GB/s before memory saturation, minimizing out-of-memory errors in memory-intensive tasks like fine-tuning. VRAM capacity reinforces this, as 16 GB on the RTX 5080 supports larger models compared to 8 to 11 GB on the GTX 1080 Ti. Power draw doubles from 180W to 360W, reflecting the performance gains but requiring robust cooling in cloud instances.

The GTX 1080 Ti suits legacy applications constrained by Pascal-specific optimizations or software lacking Blackwell support. Its 180W TDP enables deployment in low-power cloud instances where 360W exceeds limits. At $0.60 per hour, it provides value for intermittent, low-intensity tasks like basic scientific computing on modest datasets.

The RTX 5080 excels in modern AI pipelines demanding high throughput, such as LLM training or Stable Diffusion generation. Its 56.3 TFLOPS FP32 performance and 960 GB/s bandwidth handle large batch sizes efficiently. Cloud pricing from $0.25 per hour makes it economical for sustained, compute-heavy workloads across four providers.