Huawei Atlas 300V Pro vs Huawei Ascend 910A

The Atlas 300V Pro is the newer part: Da Vinci, launched in 2022, against the Ascend 910A's Da Vinci 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 Atlas 300V Pro against the Ascend 910A: FP16 70 vs 256 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 48 GB against 32 GB, fed at 205 GB/s versus 1.2 TB/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.

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.