As enterprise AI adoption continues to accelerate, the need for robust, independent, and cost-effective infrastructure has never been greater. In this comprehensive benchmark, we evaluate the performance of deploying the DeepSeek R1 large language model on Huawei Ascend Atlas 800 training servers.
The Setup: Ascend Atlas 800
Our test environment utilized a cluster of Atlas 800 servers, featuring the Ascend 910 processor natively designed for deep learning operations. The software stack leveraged the Compute Architecture for Neural Networks (CANN) framework, optimizing the tensor operations required by DeepSeek's massive parameter architecture.
Key Hardware Specs
- Processors: Ascend 910 NPU
- Memory: 32GB HBM per NPU
- Network: 100G RoCE for high-speed internode communication
"The ability to run state-of-the-art open-source text generation models entirely independently from traditional supply chains offers a profound strategic advantage."
Performance Results
The tuning process involved optimizing mixed-precision training and optimizing pipeline parallelism through the MindSpore integration. Once fine-tuned, the throughput metrics exceeded our initial estimates. The tokens processed per second (TPS) ratio was exceptionally competitive with industry-standard counterparts, especially when accounting for the significantly lower Total Cost of Ownership (TCO).
Conclusion
Deploying cutting-edge models like DeepSeek on Huawei Ascend infrastructure is not merely a viable alternative—it frequently proves to be the superior choice for enterprises where data sovereignty, predictable operational costs, and supply chain independence are paramount requirements.