Core Insights - The continuous decrease in inference costs of large models and rapid development of domestic chips are driving the localization and deployment of inference models in data-sensitive industries such as government, finance, and healthcare [1] - The "XunTest" tool developed by China Mobile Qilu Innovation Institute addresses the challenge of efficiently and accurately testing model inference performance, which is crucial for selecting high-performance and low-cost chips [1][3] Group 1 - The "XunTest" tool innovatively constructs a "configuration equals testing" model and integrates powerful automatic data parsing capabilities, significantly improving chip selection efficiency [1][3] - The application of the "XunTest" tool has led to a remarkable transformation in efficiency, reducing the average manual monitoring time for a single test from 8 hours to 0.5 hours and decreasing data organization time by 70%, resulting in an overall chip selection efficiency improvement of 3 times [1][3] Group 2 - The core competitiveness of "XunTest" lies in two major technological highlights: intelligent automatic testing based on vLLM and automatic summarization and visualization of testing data [3] - The tool supports both local and remote testing modes, allowing flexible adaptation to different chip deployment needs, and enables engineers to initiate full-process tasks with a single configuration, overcoming the inefficiencies of traditional manual testing [3] - A standardized data storage mechanism is employed to automatically calculate and generate key performance indicators, ensuring comparability of test results across different chip platforms and heterogeneous environments [3]