NVIDIA GeForce RTX 2080 Ti 11GB vs NVIDIA Quadro RTX 5000 16GB

Both GPUs launched in 2018: the GeForce RTX 2080 Ti on Turing and the Quadro RTX 5000 on Turing.

Dense throughput for the GeForce RTX 2080 Ti against the Quadro RTX 5000: FP64 0.4 vs 0.3 TFLOPS, FP32 14 vs 11 TFLOPS, FP16 57 vs 89 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 11 GB against 16 GB, fed at 616 GB/s versus 448 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 260 W for the GeForce RTX 2080 Ti and 265 W for the Quadro RTX 5000. At FP32 that works out to 0.05 against 0.04 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.