NVIDIA A10G 24GB vs NVIDIA GeForce RTX 3090 24GB

The A10G is the newer part: Ampere, launched in 2022, against the GeForce RTX 3090's Ampere from 2020. 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 A10G against the GeForce RTX 3090: FP32 35 vs 36 TFLOPS, FP16 70 vs 71 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 24 GB against 24 GB, fed at 600 GB/s versus 936 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 300 W for the A10G and 350 W for the GeForce RTX 3090. At FP32 that works out to 0.12 against 0.10 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.