RTX 4080 vs RTX 5080

The RTX 5080 surpasses the RTX 4080 in raw compute capability: its 56.3 TFLOPS in FP16 and FP32 exceeds the RTX 4080's 48.7 TFLOPS by 15.6 percent, accelerating matrix operations central to deep learning. This uplift translates to faster LLM training epochs and inference queries, particularly in FP16-optimized frameworks like TensorRT. Memory bandwidth presents a larger gap at 960 GB/s for the RTX 5080 versus 717 GB/s for the RTX 4080, a 33.9 percent improvement that supports larger batch sizes during training and reduces bottlenecks in data-heavy inference. Both GPUs constrain workloads to 16 GB VRAM, limiting model sizes equally, yet the RTX 5080's GDDR7 sustains higher throughput for sustained loads. Power draw rises to 360W on the RTX 5080 from 320W on the RTX 4080, reflecting denser compute at the cost of 12.5 percent higher TDP. These specs position the RTX 5080 for demanding AI pipelines where bandwidth and FLOPS dominate runtime.

The RTX 4080 suits budget-limited projects requiring solid performance without premium costs. At $0.11 per hour starting price and $0.28 per hour average across 8 offers, it undercuts the RTX 5080's $0.25 per hour entry by 56 percent, ideal for prototyping, small-scale fine-tuning, or Stable Diffusion generation where 48.7 TFLOPS FP16 suffices. Lower 320W TDP also aids deployments sensitive to power constraints or multi-GPU setups sharing resources.

Opt for the RTX 5080 in performance-critical scenarios demanding the latest architecture. Its 56.3 TFLOPS FP16 and 960 GB/s bandwidth outperform the RTX 4080 by 15.6 percent and 33.9 percent respectively, excelling in large-batch LLM training or high-throughput inference. Blackwell's 2025 advancements future-proof investments for evolving AI models despite the $0.38 per hour average pricing.