[SMM Flash] Langfang City establishes a four-voucher linkage system of "computing power vouchers + token vouchers + data vouchers + model vouchers"

Published: Sep 8, 2026 10:56

Langfang NDRC solicited public comments on September 4 regarding the "Four Measures to Guide the Innovative Development of the Computing Power Industry," establishing a multi-voucher linked policy system of "computing power vouchers + token vouchers + data vouchers + model vouchers" to guide computing power in empowering the real economy.

[Data]

- Computing power vouchers: no more than 10 million yuan allocated annually, subsidizing 50% of computing power resource costs, with a maximum of 1 million yuan per unit per year

- Token vouchers: no more than 4 million yuan annually, supporting token factories/platforms, with a maximum of 1 million yuan per entity

- Data vouchers: no more than 2 million yuan annually, supporting corpus/dataset development

- Model vouchers: included in the multi-voucher system (specific amounts to be supplemented)

- Comment solicitation period: 2026-09-04 to 2026-10-03

[SMM Comment] Viewpoint: The four-voucher linkage extends subsidies from "computing power usage" to the entire chain of "data/models/tokens," reducing the comprehensive cost of AI applications and stimulating small and medium-sized enterprises to adopt cloud services and utilize data. Impact pathway: subsidies → increased computing power demand from SMEs → improved regional computing power absorption. Comparative factors: Heilongjiang computing power vouchers (deduction ≤50%, maximum 4 million yuan per year, issued on April 23) and Guizhou computing power vouchers 3.0 (30%) create regional investment competition. Key data: computing power vouchers at 50%/10 million yuan.

Data Source Statement: Except for publicly available information, all other data are processed by SMM based on publicly available information, market communication, and relying on SMM's internal database model. They are for reference only and do not constitute decision-making recommendations.

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