NVIDIA GeForce RTX 2060 6GB vs NVIDIA Titan RTX 24GB

The GeForce RTX 2060 is the newer part: Turing, launched in 2019, against the Titan RTX's Turing from 2018. 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 2060 against the Titan RTX: FP64 0.2 vs 0.5 TFLOPS, FP32 6.5 vs 16 TFLOPS, FP16 26 vs 131 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 6 GB against 24 GB, fed at 336 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 160 W for the GeForce RTX 2060 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.