RTX 3080 vs RTX 4080

The RTX 4080 demonstrates superior raw compute with 48.7 TFLOPS in FP16 and FP32, a 63 percent increase over the RTX 3080's 29.8 TFLOPS: this translates to faster matrix multiplications essential for deep learning training and inference. Training large neural networks benefits directly, as higher throughput reduces epoch times in FP16-optimized frameworks like PyTorch.

VRAM capacity impacts model handling: 16 GB on the RTX 4080 accommodates larger language models or bigger batch sizes without offloading, unlike the RTX 3080's 10-12 GB limit which constrains scale. Inference workloads see similar gains, with the RTX 4080 serving more requests per second due to elevated FP32 performance.

Memory bandwidth presents a nuanced difference, 760 GB/s on RTX 3080 exceeding 717 GB/s on RTX 4080: higher bandwidth supports larger effective batch sizes in bandwidth-bound scenarios like certain convolutional networks. Both share 320W TDP, ensuring comparable power envelopes in cloud instances.

The RTX 3080 excels in cost-sensitive deployments where 10-12 GB GDDR6X VRAM suffices for models under that threshold. Its $0.06/hr starting price and $0.15/hr average across 10 offers undercut the RTX 4080 by over 50 percent, ideal for prototyping or high-volume inference on smaller datasets.

Scenarios favoring bandwidth include memory-intensive tasks leveraging 760 GB/s throughput, such as image processing pipelines with large batches. Availability across more providers enhances reliability for interruptible workloads.

Opt for the RTX 4080 when VRAM demands exceed 12 GB, as its 16 GB GDDR6X enables training or inference on larger models without quantization. The 48.7 TFLOPS FP16/FP32 performance accelerates workloads by 63 percent over the RTX 3080's 29.8 TFLOPS, justifying $0.28/hr average cost for production environments.

Ada Lovelace architecture provides efficiency gains in ray tracing and tensor cores, benefiting generative AI and scientific simulations requiring sustained high compute.