RTX 2080 vs RTX 4080 SUPER

The RTX 4080 SUPER demonstrates superior raw compute power with 48.7 TFLOPS in FP16 and FP32, a 4.8 times increase over the RTX 2080's 10.1 TFLOPS in each. This delta translates to faster model training and inference: training epochs complete in roughly one-fifth the time on the RTX 4080 SUPER for FP16-optimized workloads common in deep learning.

Memory bandwidth of 717 GB/s on the RTX 4080 SUPER exceeds the RTX 2080's 616 GB/s by 16 percent, enabling larger batch sizes without memory bottlenecks. For instance, inference on large language models benefits from sustained throughput at batch sizes exceeding those feasible on 8 to 11 GB VRAM setups.

The doubled VRAM capacity at 16 GB on the RTX 4080 SUPER supports larger models outright, reducing swapping in tasks like fine-tuning. Higher TDP at 320 W reflects greater capability, though both use PCIe form factors. NVLink on the RTX 2080 aids multi-GPU scaling absent on the RTX 4080 SUPER.

The RTX 2080 suits low-budget deployments where costs must stay below $0.10 per hour. Its pricing from $0.05 per hour makes it ideal for prototyping small models fitting within 8 to 11 GB VRAM, such as lightweight inference or basic Stable Diffusion runs.

Users with 215 W power constraints or NVLink multi-GPU needs select the RTX 2080 for legacy Turing-optimized codebases. It delivers adequate 10.1 TFLOPS for non-demanding scientific computing without overprovisioning.

The RTX 4080 SUPER excels in performance-critical applications requiring 48.7 TFLOPS FP16 or FP32 throughput. Its 16 GB VRAM handles large-scale LLM fine-tuning or training where the RTX 2080's 8 to 11 GB falls short.

Higher 717 GB/s bandwidth supports high-batch inference, justifying $0.17 per hour starting costs for production workloads. Ada Lovelace features enhance efficiency in modern AI pipelines despite 320 W TDP.