RTX 4070 vs RTX 5080

The RTX 5080 delivers nearly double the compute power of the RTX 4070: 56.3 TFLOPS in FP16 and FP32 compared to 29.1 TFLOPS. This delta translates to faster model training and inference, as FP16 accelerates half-precision operations common in deep learning, while FP32 ensures precision for scientific computing. Training large language models benefits from the RTX 5080's advantage, potentially halving iteration times on equivalent datasets.

Memory bandwidth impacts batch sizes directly: the RTX 5080's 960 GB/s supports larger batches than the RTX 4070's 504 GB/s, reducing overhead in inference pipelines and enabling handling of bigger models without swapping. The RTX 5080's 16 GB VRAM versus 12 GB accommodates larger contexts in LLMs, minimizing out-of-memory errors. However, the RTX 5080's 360W TDP exceeds the RTX 4070's 200W, increasing power costs in prolonged cloud sessions.

In real-world scenarios, the RTX 5080 excels in throughput-heavy tasks like Stable Diffusion generation, where higher bandwidth sustains high-resolution outputs.

The RTX 4070 suits budget-limited projects: its cloud pricing starts at $0.07 per hour, averaging $0.19 per hour across 9 offers, far below the RTX 5080's $0.25 to $0.38 per hour. Lower 200W TDP reduces operational costs for extended light workloads such as fine-tuning small models or basic inference, where 12 GB VRAM and 29.1 TFLOPS suffice without excess capacity.

Opt for the RTX 5080 in performance-critical applications: 56.3 TFLOPS FP16/FP32 doubles the RTX 4070's speed for LLM training and inference. Its 16 GB GDDR7 VRAM and 960 GB/s bandwidth handle large batch sizes and complex models effectively, justifying the higher $0.38 per hour average for time-sensitive tasks.