RTX 4080 SUPER vs Tesla T4

The RTX 4080 SUPER outperforms the T4 substantially in raw compute: its 48.7 TFLOPS for FP16 and FP32 dwarfs the T4's 8.1 TFLOPS, enabling up to six times faster matrix operations critical for deep learning. This delta translates to quicker training epochs and higher inference throughput for models like transformers, where half-precision computations dominate. The Ada Lovelace architecture's tensor cores further accelerate these workloads beyond the Turing design.

Memory bandwidth reveals another gap: 717 GB/s on the RTX 4080 SUPER versus 320 GB/s on the T4 supports larger batch sizes without bottlenecks, ideal for processing extensive datasets in fine-tuning or inference. However, the RTX 4080 SUPER's 320 W TDP contrasts sharply with the T4's 70 W, limiting the latter to power-constrained deployments but allowing dense packing. In real-world terms, the RTX 4080 SUPER handles modern LLMs with batch sizes twice as large due to superior bandwidth and compute.

Select the RTX 4080 SUPER for compute-intensive tasks such as LLM training or Stable Diffusion generation, where its 48.7 TFLOPS FP16 performance delivers results six times faster than the T4's 8.1 TFLOPS. Its 717 GB/s memory bandwidth enables larger batch sizes, reducing training time for models requiring 16 GB VRAM. At $0.17 per hour starting price, it offers superior value for performance-driven cloud workloads.

Choose the T4 in power-sensitive environments like edge inference servers, where its 70 W TDP allows higher density than the RTX 4080 SUPER's 320 W. It suffices for lightweight inference on legacy models fitting within 16 GB GDDR6 and 320 GB/s bandwidth. Despite higher pricing from $0.53 per hour, its Turing optimizations suit low-latency, multi-GPU inference clusters.