NVIDIA A30 24GB vs NVIDIA A10G 24GB

The A10G is the newer part: Ampere, launched in 2022, against the A30's Ampere from 2021. 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 A30 against the A10G: FP32 10 vs 35 TFLOPS, FP16 165 vs 70 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 933 GB/s versus 600 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 165 W for the A30 and 300 W for the A10G. At FP32 that works out to 0.06 against 0.12 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.