Huawei Atlas 350 vs Huawei Ascend 950DT
The Ascend 950DT delivers 29% more FP8 throughput than the Atlas 350 (1,034 vs 804 TFLOPS dense). The Ascend 950DT also carries 32 GB more memory (144 GB vs 112 GB).
Huawei Atlas 350
Full specs →Huawei Ascend 950DT
Full specs →Specifications
Performance (TFLOPS)
FLOPS by Precision
What actually differs
Both GPUs launched in 2026: the Atlas 350 on Da Vinci v3 and the Ascend 950DT on Da Vinci v3.
Dense throughput for the Atlas 350 against the Ascend 950DT: FP16 425 vs 547 TFLOPS, FP8 804 vs 1,034 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.
Memory is 112 GB against 144 GB, fed at 1.4 TB/s versus 4.0 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.
Frequently asked questions
Is the Atlas 350 faster than the Ascend 950DT?
At FP8 precision the Ascend 950DT reaches 1,034 TFLOPS dense against 804 TFLOPS for the Atlas 350. The performance table on this page lists every published precision for both GPUs.
Which has more memory, the Atlas 350 or the Ascend 950DT?
The Ascend 950DT carries 144 GB of memory versus 112 GB for the Atlas 350. Memory bandwidth is 1.4 TB/s for the Atlas 350 and 4.0 TB/s for the Ascend 950DT.
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