Huawei Ascend 910A vs Huawei Atlas 300V Pro

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 Ascend 910A against the Atlas 300V Pro: FP16 256 vs 70 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

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

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.