NVIDIA Quadro RTX 5000 16GB vs NVIDIA Titan RTX 24GB

Both GPUs launched in 2018: the Quadro RTX 5000 on Turing and the Titan RTX on Turing.

Dense throughput for the Quadro RTX 5000 against the Titan RTX: FP64 0.3 vs 0.5 TFLOPS, FP32 11 vs 16 TFLOPS, FP16 89 vs 131 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 448 GB/s versus 672 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 265 W for the Quadro RTX 5000 and 280 W for the Titan RTX. At FP32 that works out to 0.04 against 0.06 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.