NVIDIA GeForce RTX 4080 16GB vs NVIDIA GeForce RTX 4090 24GB

Both GPUs launched in 2022: the GeForce RTX 4080 on Ada Lovelace and the GeForce RTX 4090 on Ada Lovelace.

Dense throughput for the GeForce RTX 4080 against the GeForce RTX 4090: FP64 0.7 vs 1.3 TFLOPS, FP32 49 vs 83 TFLOPS, FP16 97 vs 165 TFLOPS, FP8 195 vs 330 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 16 GB against 24 GB, fed at 717 GB/s versus 1.0 TB/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 450 W for the GeForce RTX 4090. At FP32 that works out to 0.15 against 0.18 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.