SenseTime has launched the Galaxy Project, teaming with nearly 20 partners to scale domestic AI chip infrastructure in China.
In a keynote titled ‘Intelligent Transformation and Symbiosis,’ Yang Fan – the company’s co-founder and president of its Large Device Business Group – laid out what SenseTime describes as a closed loop connecting chip-level technology, ecosystem partnerships, and commercial deployment for domestically-produced AI computing power.
Alongside the Galaxy Project, SenseTime signed a space computing agreement with satellite manufacturer Guoxing Aerospace and struck a research partnership with five institutions – including the Shanghai Artificial Intelligence Laboratory – aimed at scientific computing applications.
Yang framed the timing around three converging trends: token demand climbing across enterprise deployments, industrial AI adoption catching up with consumer-facing use cases, and domestic chip commercialisation reaching a point where intelligent computing centres built on Chinese silicon can be stood up at pace.
However, whether that window is as open as SenseTime claims depends heavily on numbers the company has not had independently verified.
Token throughput figures come with a large asterisk
SenseTime says its large-scale device platform now processes an average of 2.42 trillion tokens daily, and the company projects that figure will climb 25-fold to 10 trillion tokens per day by the fourth quarter of 2026. That’s a forecast, not a measured result, and enterprise buyers evaluating SenseTime’s infrastructure should treat it as such until quarterly figures start landing.
The cost-effectiveness claims attached to that growth are similarly self-reported. SenseTime says its heterogeneous hybrid inference technology delivers an 85–152 percent increase in Model FLOPs Utilisation on mainstream domestic chips, alongside inference cost-effectiveness the company puts at 1.25x that of Nvidia’s H-series parts.
Compared with domestic homogeneous inference setups, SenseTime claims a 2.5x increase in token output at equivalent cost, a jump it says pushes optimised hybrid inference clusters past what the industry previously regarded as the minimum profitability threshold for domestic computing power.
None of these figures come with third-party benchmarking, and the gap between a vendor’s optimised test cluster and a customer’s production environment – with its uneven data pipelines and delayed firmware updates – tends to be where such numbers soften.
Adaptability claims and the multi-chip problem
Domestic AI chips have historically struggled with a fragmented software stack: models trained for one architecture often require rework to run on another. SenseTime says it has built a full-stack adaptation layer spanning models, frameworks, operators, toolchains, and hardware to address that, with the aim of letting customers migrate workloads across domestic chip vendors without extensive rewrites.
The company…
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