RTX 4070 SUPER vs RTX 5080

Compute performance defines the core disparity: the RTX 5080 achieves 56.3 TFLOPS in FP16 and FP32, surpassing the RTX 4070 SUPER's 29.1 TFLOPS by 94 percent. In training, this accelerates gradient computations; for inference, it boosts query throughput in half-precision pipelines common to LLMs. Equivalent FP16 and FP32 rates on both ensure balanced tensor core utilization without precision bottlenecks. Memory bandwidth critically impacts real-world efficiency: 960 GB/s on the RTX 5080 versus 504 GB/s on the RTX 4070 SUPER doubles data transfer rates, enabling larger batch sizes in training and reducing stalls in inference for memory-bound models. The RTX 5080's 16 GB VRAM accommodates models exceeding 12 GB, avoiding out-of-memory errors in fine-tuning large transformers. Higher 360W TDP on the RTX 5080 sustains peak clocks longer than the 200W RTX 4070 SUPER, though it demands robust power and cooling.

The RTX 4070 SUPER suits budget-conscious or power-limited deployments. Its 200W TDP integrates easily into compact systems or edge computing with constrained PSUs. The 12 GB VRAM and 29.1 TFLOPS handle fine-tuning smaller LLMs or Stable Diffusion at viable speeds. Absence of live cloud offers favors on-premises users avoiding rental costs.

Opt for the RTX 5080 in performance-critical scenarios. Its 56.3 TFLOPS and 960 GB/s bandwidth excel in LLM training with large batches or high-volume inference. Cloud access from $0.25 per hour supports scalable, on-demand workloads without hardware ownership. Extra 16 GB VRAM fits expansive models seamlessly.