RTX 3080 vs T4

The RTX 3080's 29.8 TFLOPS FP16 and FP32 ratings dwarf the T4's 8.1 TFLOPS in both metrics, enabling approximately 3.7 times faster matrix operations critical for deep learning training and inference. Training large models benefits immensely from this compute advantage, as epochs complete quicker on the RTX 3080. Inference workloads similarly accelerate, processing more samples per second.

Memory bandwidth presents another stark difference: 760 GB/s on the RTX 3080 versus 320 GB/s on the T4 supports larger batch sizes without bottlenecks in data transfer. This allows the RTX 3080 to handle bigger batches in training loops, reducing per-sample overhead. The T4's lower bandwidth limits scalability for high-throughput scenarios.

VRAM capacity tilts toward the T4 with 16 GB GDDR6 against 10 to 12 GB GDDR6X on the RTX 3080, accommodating larger models or batches before out-of-memory errors occur. Power draw of 320 W for the RTX 3080 versus 70 W for the T4 affects deployment density: multiple T4s fit in low-power servers, while the RTX 3080 suits high-performance single-GPU tasks.

Opt for the RTX 3080 in performance-critical workloads like model training or generative AI where 29.8 TFLOPS FP32 outperforms the T4's 8.1 TFLOPS by over 3.6 times. Its 760 GB/s bandwidth excels with large batch sizes, and cloud pricing from $0.06 per hour delivers unmatched value for speed-focused projects.

Budget-conscious users benefit from the average $0.15 per hour rate across 10 offers, ideal for rapid prototyping or compute-heavy simulations.

Choose the T4 for memory-bound inference on large models requiring 16 GB VRAM, exceeding the RTX 3080's 10 to 12 GB capacity. Its 70 W TDP enables dense deployments in power-constrained environments.

Enterprise settings favor the T4 for reliable, low-latency inference at scale, despite higher $0.53 per hour starting pricing.