Large model funding warms up as AI Hub launches enterprise deployment acceleration solution

1. Policy dividends drive recovery in large model investment and financing

In October 2026, five ministries including the Ministry of Industry and Information Technology and the Ministry of Science and Technology jointly issued the "Artificial Intelligence Large Model Industry Application Promotion Policy", which explicitly offers tax reductions, R&D subsidies, and scenario access support for large model deployment in vertical sectors such as healthcare, finance, and manufacturing. After the policy was announced, financing events in the primary market for large model tracks grew by about 30% month-on-month, with several leading funds refocusing on foundational models and industry-specific models. Industry analysts believe the policy signal has restored investor confidence, particularly in companies with the ability to achieve commercial closed loops. Meanwhile, local governments are racing to build intelligent computing centers to reduce enterprise computing costs and provide infrastructure support for large model applications.

2. Enterprise deployment bottlenecks: dual challenges of cost and compliance

Despite favorable policies, most enterprises still face two major hurdles when deploying large models. First, inference costs remain high: the price of a single call to a high-performance model is still a burden for small and medium-sized enterprises, and large-scale adoption requires fine-grained control of token expenses. Second, compliance requirements are tightening: data privacy, model output controllability, and industry regulatory details have become obstacles. Some enterprises have attempted to build their own models, but training and operations costs far exceeded expectations, causing projects to stall. The industry urgently needs an intermediary service platform that offers low-cost API calls while ensuring compliance — precisely the focus of this AI hub upgrade.

3. AI Hub launches acceleration solution: token wholesale + channel agents as dual engines

In response to these pain points, the AI Hub platform officially released its "Enterprise Large Model Deployment Acceleration Solution". The solution has two core modules:

• Token wholesale model: The platform integrates multiple major large model providers, leveraging centralized procurement and dynamic routing scheduling to offer the cheapest tokens in the industry. Enterprises can purchase token packages in advance and settle based on actual usage, reducing costs by more than 40% compared to traditional per-call billing. Additionally, the platform provides an AI API relay station mechanism that automatically distributes requests across multiple models to balance performance and cost.
• Channel agent ecosystem: AI Hub opens its AI channel agent and AI large model agent partnership system, allowing system integrators and SaaS providers to embed large model capabilities into their own products and earn revenue sharing. Through the token agent model, agent partners can offer customers a one-stop AI capability without developing their own models, significantly shortening delivery cycles. Furthermore, the platform includes built-in compliance review and content security modules to help agents meet industry regulatory requirements.

The solution went live in mid-October, with initial signings covering financial customer service, medical diagnostic assistance, and industrial quality inspection. A technical lead from a manufacturing company that participated in the beta test said: "With the help of AI Hub's token relay station service, we saved development effort from integrating multiple models and can flexibly switch models based on business peaks — the cost-effectiveness is outstanding."

4. Industry outlook: large model applications enter deeper waters

From the rebound in investment and financing, to policy implementation, and now the maturity of platform-level tools, the large model industry is moving from "technology exploration" to "large-scale application." Industry insiders expect that over the next six months, more benchmark cases will emerge in vertical scenarios, and reducing token costs while ensuring data compliance remain key decisive factors. The AI Hub platform stated that the next phase will focus on expanding joint fine-tuning services for industry-specific models and bringing more mid-to-long-tail AI companies into the AI channel agent network, continuing to drive the democratization of large models. Additionally, the platform is in talks with multiple computing service providers about wholesale intelligent computing resources, which could further lower inference infrastructure costs. For enterprises at large, the fourth quarter of 2026 may be the optimal window to adopt large model capabilities.