RTX 3090 vs T4

The FP16 dense figure of 71 TFLOPS on the RTX 3090 exceeds the 65 TFLOPS dense FP16 on the T4 while FP32 performance shows 35.6 TFLOPS against 8.1 TFLOPS. This ratio indicates the RTX 3090 sustains roughly four times the FP32 throughput of the T4 for mixed precision workloads. Training sessions benefit when dense FP16 operations align with the higher 936 GB/s bandwidth to support larger batch sizes than the 320 GB/s limit allows. Inference tasks compare dense INT8 throughput at 284 TOPS for the RTX 3090 to 130 TOPS for the T4. Sparsity doubles these values to 568 TOPS and remains unavailable for the T4. Memory capacity of 24 GB versus 16 GB further extends the RTX 3090 advantage in holding larger models without swapping. The T4 maintains efficiency through its 70W TDP during sustained dense FP16 inference at 65 TFLOPS. Bandwidth constraints on the T4 reduce effective batch sizes in memory intensive operations compared to the RTX 3090.

The RTX 3090 suits workloads that require 24 GB VRAM and 936 GB/s bandwidth for handling large models. FP32 performance at 35.6 TFLOPS combined with NVLink interconnect supports distributed training sessions where the T4 cannot match capacity or speed.

The T4 fits deployments constrained by 70W TDP and PCIe 3.0 interconnect when dense FP16 at 65 TFLOPS meets inference needs. Its 16 GB VRAM handles moderate batch sizes efficiently without exceeding power limits of the RTX 3090.