{"id":114858,"date":"2026-07-22T12:58:20","date_gmt":"2026-07-22T12:58:20","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/114858\/"},"modified":"2026-07-22T12:58:20","modified_gmt":"2026-07-22T12:58:20","slug":"sensetimes-galaxy-project-targets-domestic-ai-chip-scale-up","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/114858\/","title":{"rendered":"SenseTime’s Galaxy Project targets domestic AI chip scale-up"},"content":{"rendered":"

SenseTime<\/a> has launched the Galaxy Project, teaming with nearly 20 partners to scale domestic AI chip infrastructure in China.<\/p>\n

In a keynote titled \u2018Intelligent Transformation and Symbiosis,\u2019 Yang Fan \u2013 the company\u2019s co-founder and president of its Large Device Business Group \u2013 laid out what SenseTime describes as a closed loop connecting chip-level technology, ecosystem partnerships, and commercial deployment for domestically-produced AI computing power.<\/p>\n

Alongside the Galaxy Project, SenseTime signed a space computing agreement with satellite manufacturer Guoxing Aerospace and struck a research partnership with five institutions \u2013 including the Shanghai Artificial Intelligence Laboratory \u2013 aimed at scientific computing applications.<\/p>\n

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.<\/p>\n

However, whether that window is as open as SenseTime claims depends heavily on numbers the company has not had independently verified.<\/p>\n

Token throughput figures come with a large asterisk<\/p>\n

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\u2019s a forecast, not a measured result, and enterprise buyers evaluating SenseTime\u2019s infrastructure should treat it as such until quarterly figures start landing.<\/p>\n

The cost-effectiveness claims attached to that growth are similarly self-reported. SenseTime says its heterogeneous hybrid inference technology delivers an 85\u2013152 percent increase in Model FLOPs Utilisation on mainstream domestic chips, alongside inference cost-effectiveness the company puts at 1.25x that of Nvidia\u2019s H-series parts.<\/p>\n

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.<\/p>\n

None of these figures come with third-party benchmarking, and the gap between a vendor\u2019s optimised test cluster and a customer\u2019s production environment \u2013 with its uneven data pipelines and delayed firmware updates \u2013 tends to be where such numbers soften.<\/p>\n

Adaptability claims and the multi-chip problem<\/p>\n

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.<\/p>\n

The company points to two applied examples. In an AI4S long-sequence protein prediction workload, SenseTime says fused operator optimisation cut overall prediction time by a factor of three. In AIGC video generation, it claims a 93 percent multi-card parallel acceleration ratio for domestic chips running DiT models, alongside what it describes as zero-cost migration for mainstream AI development tools.<\/p>\n

These are the kinds of figures that read well in a sandbox test and matter far more once they\u2019re stress-tested against real customer pipelines running mixed hardware generations.<\/p>\n

Energy metrics get a new benchmark name<\/p>\n

SenseTime introduced a metric it calls Tokens Per Watt, positioned as a replacement yardstick for measuring AI data centre efficiency, alongside a Computing-Power Collaboration Agent that handles resource scheduling, electricity price prediction, and energy storage optimisation across what the company describes as an eight-level data system with five decision chains.<\/p>\n

Combining compute, electricity pricing, and automated scheduling, SenseTime claims an 80 percent increase in token output per unit of electricity cost, average power prices 10 percent below comparable regional data centres, and 96 percent accuracy in computing load prediction.<\/p>\n

These are claims worth watching over the next several quarters rather than accepting at face value. Electricity price arbitrage and load forecasting accuracy tend to perform differently once a system runs through a full seasonal cycle with genuine demand volatility, rather than the conditions under which a vendor typically runs its pilot.<\/p>\n

Impressive partner roster spans chipmakers to component suppliers<\/p>\n

The Galaxy Project\u2019s stated ecosystem includes domestic chip vendors Cambricon, Muxi, Hygon, Huawei Ascend, Moore Threads, Sunrise, and Biren Technology, component partner Xizhi Technology, and infrastructure firms including Silicon Motion, Qujing Technology, Zhongke Jiahe, Qingcheng Jizhi, Sophon Information, and Jiliu Technology.<\/p>\n

SenseTime says the plan covers construction of one \u201ctoken factory,\u201d five computing clusters at what it calls \u201c10,000-calorie\u201d scale, joint work across ten technology directions, and support for 200 AI startups.<\/p>\n

\u201cDomestic production is not simply about replacing individual chips, but rather a collaborative effort across the entire chain of China\u2019s innovation capabilities, from chips and components to infrastructure and application scenarios,\u201d Yang said.<\/p>\n

Space, optical, and quantum computing bets look further out<\/p>\n

Beyond near-term infrastructure, SenseTime outlined work on optical computing for data centre efficiency, quantum computing applications in AI optimisation, and a space computing partnership with Guoxing Aerospace to build what the two companies call the SenseTime Space Computing Constellation.<\/p>\n

SenseTime\u2019s plan calls for a first satellite launch in 2026, building toward thousands of computing satellites and computing capacity in the tens of thousands of petabytes by 2030.<\/p>\n

Yang argued the value extends past raw capability, framing space-based computing as a way to extend the reach of Chinese AI services into weak-network environments such as maritime operations and disaster response, and by extension to support China\u2019s AI exports internationally.<\/p>\n

That 2030 target sits five years out, and satellite computing deployments of this scale have no precedent to measure the timeline against.<\/p>\n

Physical infrastructure spans Shanghai to Riyadh<\/p>\n

On the ground, SenseTime says its Shanghai facility runs the country\u2019s first data centre rated at what it calls \u201c5A\u201d intelligent computing level, handling over 20 trillion tokens daily across more than 20 industries. A Yancheng site has launched with an initial 3,000 petaflops of capacity focused on energy, manufacturing, and low-altitude economy applications.<\/p>\n

In Hong Kong, SenseTime is building what it describes as the territory\u2019s largest domestic intelligent computing centre, targeting 40,000 petaflops by 2030. The company also plans what it calls China\u2019s first overseas domestic computing cluster in Saudi Arabia, positioned as a full-stack domestic computing base for the Middle East.<\/p>\n

On the research side, SenseTime\u2019s tie-up with the Shanghai AI Laboratory, Beijing Zhongguancun Academy, Shenzhen Hetao Academy, the Shanghai Algorithm Innovation Research Institute, and Shanghai Jiao Tong University\u2019s AI school aims to build a shared platform spanning compute, tooling, and model capability for life sciences, materials science, and manufacturing research. Yang called AI for Science \u201ca key lever for paradigm innovation in basic research,\u201d tying the initiative to China\u2019s broader \u201cArtificial Intelligence+\u201d policy push.<\/p>\n

SenseTime\u2019s forecast of 10 trillion tokens per day by Q4 2026 is the figure to track against whatever the company reports when that quarter actually closes.<\/p>\n

See also: Kimi K3 open-weight model: China\u2019s biggest AI is a bet on memory, not compute<\/a><\/p>\n

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