NVIDIA GeForce RTX 2060 12GB vs NVIDIA GeForce RTX 2080 Super 8GB

The GeForce RTX 2060 is the newer part: Turing, launched in 2021, against the GeForce RTX 2080 Super's Turing from 2019. 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 GeForce RTX 2080 Super: FP64 0.2 vs 0.3 TFLOPS, FP32 7.2 vs 11 TFLOPS, FP16 29 vs 45 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 12 GB against 8 GB. 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 185 W for the GeForce RTX 2060 and 250 W for the GeForce RTX 2080 Super. At FP32 that works out to 0.04 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.