Lingsi Raised ₽35 Billion for On-Device AI Chips
Startup Lingsi Tech completed a Series B funding round of approximately 500 million yuan (roughly ₽35 billion). The company develops specialized chips for running AI models directly on smartphones, laptops, and IoT devices — without sending data to the cloud. State investment funds from Anhui Province and Hefei City led the round. Main focus: transition from chips for simple perception models to chips capable of executing large language models.
AI-processed from 36Kr (36氪); edited by Hamidun News
Lingsi Tech, a Chinese startup specializing in microchips for local AI inference, raised funding in a Series B round totaling approximately 500 million yuan (roughly ₽35 billion). Investment came from state investment funds from Anhui Province and Hefei City, as well as from venture capital funds: Deep Report Capital, Sky Intelligence Ventures, Scientia Venture, Yingke Capital, Dongrui Capital, and Yongxin Ark. Taihé Capital was appointed as the financial advisor to the company.
Where the Investments Will Go
Lingsi will direct the attracted funds toward developing the next generation of chips for edge AI inference — running neural network models on peripheral devices. Specifically, the company plans to evolve from its current specialization in chips for perception models (image recognition, sensor data processing) to supporting large language models and reasoning systems that execute directly on the user's device. This enables users to run powerful AI systems locally, without relying on cloud connectivity.
- Series B round: approximately 500 million yuan (≈₽35 billion)
- Primary investors: state funds from Anhui and Hefei
- Current direction: edge inference for LLM and cognitive-level models
- Product evolution: from perception models to cognition-oriented chips
Why This Matters for the AI Market
Edge computing chips are winning market share as cloud computing faces challenges with latency, privacy, and cost. Users and corporations demand that critical data and models run locally. Lingsi competes with major players like Qualcomm, Apple (Neural Engine), and NVIDIA, but targets the Asian market and specialized applications. The attraction of state investment demonstrates that edge AI is viewed as a strategic area even at the government level.
What It Means
Growth in edge AI inference investment signals the establishment of local AI inference as a separate segment of the microchip market. Instead of centralized cloud — distributed processing. For developers, this means new optimization libraries and APIs for running models on devices. For end users — faster and more private AI.
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Key Questions Answered
Where the Investments Will Go?
Lingsi will direct the attracted funds toward developing the next generation of chips for edge AI inference — running neural network models on peripheral devices. Specifically, the company plans to evolve from its current specialization in chips for perception models (image recognition, sensor data processing) to supporting large language models and reasoning systems that execute directly on the user's device. This enables users to run powerful AI systems locally, without relying on cloud connectivity. Series B round: approximately 500 million yuan (≈₽35 billion) Primary in
Why This Matters for the AI Market?
Edge computing chips are winning market share as cloud computing faces challenges with latency, privacy, and cost. Users and corporations demand that critical data and models run locally. Lingsi competes with major players like Qualcomm, Apple (Neural Engine), and NVIDIA, but targets the Asian market and specialized applications. The attraction of state investment demonstrates that edge AI is viewed as a strategic area even at the government level.
What It Means?
Growth in edge AI inference investment signals the establishment of local AI inference as a separate segment of the microchip market. Instead of centralized cloud — distributed processing. For developers, this means new optimization libraries and APIs for running models on devices. For end users — faster and more private AI.
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