RTX 3080 vs RTX 4080 SUPER

The RTX 4080 SUPER demonstrates superior raw compute with 48.7 TFLOPS in FP16 and FP32, compared to the RTX 3080's 29.8 TFLOPS, resulting in approximately 63 percent higher throughput for AI training and inference. This advantage speeds up neural network operations, particularly in half-precision formats common for large language models, allowing faster convergence during training phases.

VRAM capacity marks a key differentiator: 16 GB on the RTX 4080 SUPER supports larger models and batch sizes than the 10 to 12 GB on the RTX 3080, reducing out-of-memory errors in memory-intensive tasks like fine-tuning. Memory bandwidth stands at 760 GB/s for RTX 3080 versus 717 GB/s for RTX 4080 SUPER, potentially benefiting data-heavy workloads on the older card by enabling quicker transfers for smaller batches.

Both share 320W TDP, ensuring comparable power efficiency in PCIe deployments, though the RTX 4080 SUPER's architectural improvements yield better performance per watt in modern frameworks.

The RTX 3080 suits cost-sensitive projects where cloud pricing starts at $0.06 per hour, averaging $0.13 per hour. Its 760 GB/s memory bandwidth outperforms the RTX 4080 SUPER's 717 GB/s, making it preferable for workloads emphasizing rapid data movement, such as certain scientific simulations or inference on models fitting within 10 to 12 GB VRAM.

Opt for the RTX 4080 SUPER in performance-critical scenarios leveraging its 48.7 TFLOPS FP16 and FP32 compute, 63 percent above the RTX 3080's 29.8 TFLOPS. The 16 GB VRAM capacity excels for large-scale training or inference with complex models, justifying the higher pricing from $0.17 per hour averaging $0.32 per hour.