A detailed Chinese blog post provides a step-by-step, command-line tutorial for deploying the GLM-5.2 large language model on a dual-node Huawei Ascend 910B (Atlas 800I A2) cluster. The guide uses the vLLM-Ascend inference engine and covers both base and chat versions of the model. It is a significant signal for the Chinese AI ecosystem, demonstrating that complex, distributed inference of a major domestic LLM is becoming practical on domestic hardware. The tutorial's focus on pure shell commands and reproducible steps indicates a maturing toolchain, though it also reveals the current operational overhead. For overseas developers, this underscores the rapid progress of China's alternative AI infrastructure, which is increasingly capable of running state-of-the-art models without NVIDIA GPUs. The guide's existence suggests a growing demand for and support of domestic AI solutions, a trend with long-term implications for global supply chains and AI development strategies.
A practical guide for deploying GLM-5.2 on dual-node Huawei Ascend 910B clusters using vLLM-Ascend, highlighting the growing maturity of China's AI hardware-software stack.