RTX 4080 vs T4

The RTX 4080's 48.7 TFLOPS in FP16 and FP32 provides approximately six times the compute throughput of the T4's 8.1 TFLOPS, translating to faster model training and inference in real-world scenarios. For training large language models, this delta reduces epoch times dramatically, enabling researchers to iterate more quickly on datasets that fit within 16 GB VRAM. Inference workloads benefit similarly, with the RTX 4080 handling higher request volumes before latency increases.

Memory bandwidth plays a critical role in batch processing: the RTX 4080's 717 GB/s supports larger batch sizes without bottlenecks, ideal for optimizing throughput in production environments. The T4's 320 GB/s limits scalability for data-heavy tasks, often requiring smaller batches and longer runtimes. The 320W TDP of the RTX 4080 demands robust cooling, but its efficiency per watt surpasses the T4 in high-utilization cases, given the performance multiplier.

Power efficiency favors the T4 at 70W for always-on inference servers where idle draw matters, yet the RTX 4080's specs dominate demanding workloads.

The RTX 4080 excels in compute-intensive tasks such as training or fine-tuning models exceeding the T4's capabilities. Its 48.7 TFLOPS FP32 and 717 GB/s bandwidth enable processing large batches swiftly, reducing total cloud costs despite the 320W TDP. At $0.11 per hour average $0.28, it offers superior value for time-sensitive projects like Stable Diffusion generation or scientific simulations requiring high throughput.

The T4 suits low-power inference deployments where 70W TDP minimizes energy costs in dense server racks. Its 16 GB GDDR6 VRAM handles lightweight models efficiently at 8.1 TFLOPS FP16, sufficient for edge-like cloud inference without performance bottlenecks. Despite higher pricing from $0.53 per hour average $1.66, it integrates seamlessly into legacy systems prioritizing reliability over speed.