RTX 4070 SUPER vs Tesla T4

The RTX 4070 SUPER's 35.5 TFLOPS FP16 and FP32 ratings dwarf the T4's 8.1 TFLOPS, delivering over four times the raw compute power for both training and inference workloads. This delta means the RTX 4070 SUPER accelerates neural network operations substantially faster: training epochs complete in less time due to higher FP32 throughput, while inference latency drops for real-time applications. Memory bandwidth plays a key role: 504 GB/s on the RTX 4070 SUPER versus 320 GB/s on the T4 allows larger batch sizes without bottlenecks, enabling efficient processing of bigger models or datasets. However, the T4's 16 GB VRAM exceeds the RTX 4070 SUPER's 12 GB, supporting slightly larger models at the cost of speed. Power draw differs markedly at 220 W for the RTX 4070 SUPER against 70 W for the T4, impacting density in multi-GPU deployments.

Choose the RTX 4070 SUPER for compute-intensive tasks like model training or fine-tuning where 35.5 TFLOPS FP16/FP32 outperforms the T4's 8.1 TFLOPS by over four times. Its 504 GB/s bandwidth handles large batches effectively in local workstations. This GPU excels in Stable Diffusion generation or scientific simulations demanding high throughput.

Opt for the T4 in cost-sensitive inference scenarios, available from $0.53 per hour in cloud environments. Its 70 W TDP enables dense server deployments, and 16 GB VRAM accommodates models too large for the RTX 4070 SUPER's 12 GB. Legacy AI pipelines benefit from the T4's proven reliability.