NVIDIA GeForce RTX 4080 16GB vs NVIDIA RTX 5000 Ada 32GB

The RTX 5000 Ada is the newer part: Ada Lovelace, launched in 2023, against the GeForce RTX 4080's Ada Lovelace from 2022. Newer architectures typically add lower-precision formats and better throughput per watt, so check the precision rows your workload actually uses.

Dense throughput for the GeForce RTX 4080 against the RTX 5000 Ada: FP32 49 vs 65 TFLOPS, FP8 195 vs 522 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 16 GB against 32 GB, fed at 717 GB/s versus 576 GB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.

Power budgets are 320 W for the GeForce RTX 4080 and 250 W for the RTX 5000 Ada. At FP32 that works out to 0.15 against 0.26 TFLOPS per watt.

For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.