RTX 3070 vs T4

Dense FP16 performance favors the T4 at 65 TFLOPS over the RTX 3070 at 40.6 TFLOPS, which can shorten inference times for models that fit within the T4 16 GB VRAM. The RTX 3070 FP32 figure of 20.3 TFLOPS exceeds the T4 figure of 8.1 TFLOPS, supporting faster training iterations when full precision is required. Memory bandwidth of 448 GB/s on the RTX 3070 versus 320 GB/s on the T4 permits larger batch sizes before memory saturation occurs during either training or inference. When sparsity is enabled the RTX 3070 reaches 81.3 TFLOPS dense FP16 equivalent while the T4 figure remains at its published dense value of 65 TFLOPS. The RTX 3070 dense INT8 rating of 162.6 TOPS surpasses the T4 dense INT8 rating of 130 TOPS, which benefits quantized inference pipelines that stay within 8 GB VRAM.

The RTX 3070 suits workloads that require its 20.3 TFLOPS FP32 rating or its 448 GB/s memory bandwidth for larger batch processing. Scientific computing tasks benefit from the higher FP32 throughput relative to the T4 8.1 TFLOPS. Fine-tuning jobs that exploit the 162.6 TOPS dense INT8 or 81.3 TFLOPS sparse FP16 also align with the RTX 3070 specification set.

The T4 fits inference scenarios that utilize its 65 TFLOPS dense FP16 and 16 GB VRAM capacity. Lower power draw at 70 W TDP enables denser deployments than the 220 W TDP of the RTX 3070. LLM inference pipelines that remain within the published dense FP16 limit gain from the T4 configuration.