Huawei Ascend 950PR vs Huawei Atlas 350
The Ascend 950PR delivers 24% more FP8 throughput than the Atlas 350 (1,000 vs 804 TFLOPS dense). The Ascend 950PR also carries 16 GB more memory (128 GB vs 112 GB).
Huawei Ascend 950PR
Full specs →Huawei Atlas 350
Full specs →Specifications
Performance (TFLOPS)
FLOPS by Precision
What actually differs
Both GPUs launched in 2026: the Ascend 950PR on Da Vinci v3 and the Atlas 350 on Da Vinci v3.
Dense throughput for the Ascend 950PR against the Atlas 350: FP16 500 vs 425 TFLOPS, FP8 1,000 vs 804 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.
Memory is 128 GB against 112 GB, fed at 1.6 TB/s versus 1.4 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 Ascend 950PR faster than the Atlas 350?
At FP8 precision the Ascend 950PR reaches 1,000 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 Ascend 950PR or the Atlas 350?
The Ascend 950PR carries 128 GB of memory versus 112 GB for the Atlas 350. Memory bandwidth is 1.6 TB/s for the Ascend 950PR and 1.4 TB/s for the Atlas 350.
How much power do the Ascend 950PR and the Atlas 350 draw?
The Ascend 950PR is rated at 900 W TDP and the Atlas 350 at 600 W.
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