Huawei Ascend 910A vs Huawei Atlas 300V
The Ascend 910A delivers 5.1x the FP16 throughput of the Atlas 300V (256 vs 50 TFLOPS dense). The Ascend 910A also carries 8 GB more memory (32 GB vs 24 GB).
Huawei Ascend 910A
Full specs →Huawei Atlas 300V
Full specs →Specifications
Performance (TFLOPS)
FLOPS by Precision
What actually differs
The Atlas 300V 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: FP16 256 vs 50 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.
Memory is 32 GB against 24 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.
Frequently asked questions
Is the Ascend 910A faster than the Atlas 300V?
At FP16 precision the Ascend 910A reaches 256 TFLOPS dense against 50 TFLOPS for the Atlas 300V. The performance table on this page lists every published precision for both GPUs.
Which has more memory, the Ascend 910A or the Atlas 300V?
The Ascend 910A carries 32 GB of memory versus 24 GB for the Atlas 300V. Memory bandwidth is 1.2 TB/s for the Ascend 910A and 205 GB/s for the Atlas 300V.
How much power do the Ascend 910A and the Atlas 300V draw?
The Ascend 910A is rated at 310 W TDP and the Atlas 300V at 72 W. On FP32 throughput per watt, the Ascend 910A is the more efficient part.
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