⚛️量子位•Freshcollected in 46m
Domestic Computing Enters the Token Standardization Era
#domestic-chipsdomestic-computing-infrastructure
💡Understand why hardware alone isn't enough to scale domestic AI and why Token standardization is the next big hurdle.
⚡ 30-Second TL;DR
What Changed
Hardware performance is no longer the sole limiting factor for domestic AI.
Why It Matters
Standardization will likely accelerate the adoption of domestic chips by reducing the integration complexity for AI developers. It forces a shift from raw hardware competition to software-defined infrastructure efficiency.
What To Do Next
Evaluate your current inference stack to identify bottlenecks in hardware abstraction layers when deploying on domestic NPU/GPU clusters.
Who should care:Developers & AI Engineers
🧠 Deep Insight
Web-grounded analysis with 18 cited sources.
🔑 Enhanced Key Takeaways
- •China is actively commoditizing AI computing power by converting electricity, particularly green energy from western regions, into standardized 'tokens' for sale, significantly increasing its value by up to 22 times compared to raw power exports.
- •State-owned telecom giants like China Telecom and China Mobile are directly selling 'token packages' to consumers, positioning AI computing power as a public utility akin to mobile data, with entry-level plans available.
- •The national strategy involves building a 'national computing network' or 'computing power Internet' to integrate diverse public and private cloud computing resources, aiming for ubiquitous connectivity and flexible, efficient cross-domain scheduling of heterogeneous computing power.
- •The shift towards token standardization is driven by a recognition that AI infrastructure competitiveness now hinges on 'cluster-scale system coordination' rather than solely 'single-chip performance,' addressing issues like low GPU utilization (often below 30%) and fragmented software stacks.
- •China's approach includes leveraging its ultrahigh-voltage grid to connect abundant, low-cost green energy in the west for high-latency AI training, while performing real-time inference closer to eastern tech hubs to optimize cost and efficiency.
🛠️ Technical Deep Dive
- Token Definition: Tokens are tiny units of data derived from breaking down larger chunks of information (text, images, audio, video, sensor data). AI models process these tokens to learn relationships, enabling prediction, generation, and reasoning.
- Tokenization Process: Data is translated into tokens through various tokenization methods, which are often tailored for specific data types and use cases to reduce the vocabulary size and, consequently, the computing power required for training and inference.
- Heterogeneous Management Platforms: Platforms like Phancy Rise vGPU offer a unified software-defined control plane for AI infrastructure, providing comprehensive management across more than 10 mainstream GPU/NPU vendors (e.g., NVIDIA, Ascend, Cambricon, Hygon).
- Fine-Grained Resource Partitioning: Advanced orchestration solutions enable ultra-fine resource partitioning, including sub-GPU level compute and MB-level memory granularity slicing, to maximize utilization of heterogeneous hardware.
- Full-Stack AI Infrastructure: China Telecom's XiRang platform is structured across five coordinated layers: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Data as a Service (DaaS), Model as a Service (MaaS), and Application as a Service (SaaS), designed to unify fragmented computing and data access.
- Token Computing Methodologies: This integrative approach uses tokens as discrete computational units across distributed systems, deep learning, quantum protocols, and economic models, employing dynamic token management techniques such as idling, clustering-based aggregation, and cryptographic transformations to enhance efficiency.
- Domestic Hardware Deployment: Industrial-scale 'token factories' are being built, such as one in Wuxi initially deploying four Huawei Ascend 384 supernode servers, each equipped with Ascend 384 AI accelerator cards.
🔮 Future ImplicationsAI analysis grounded in cited sources
China will establish a dominant position in global AI compute exports.
By converting low-cost green energy into high-value AI tokens and leveraging a national computing network, China can offer highly competitive and scalable AI processing services to international users.
The commoditization of AI computing power will significantly democratize access to advanced AI capabilities.
The availability of competitively priced 'token packages' from telecom operators will lower the entry barrier for small businesses, independent developers, and users in emerging markets to utilize AI tools.
China's AI infrastructure will become deeply integrated into its national 'six networks' strategy, making AI a fundamental public utility.
The government's strategic positioning of computing power as a national infrastructure, alongside water and electricity, indicates a long-term plan for pervasive AI integration across all sectors.
⏳ Timeline
2017
China releases the Next Generation AI Development Plan, aiming for global AI leadership by 2030.
2021
China launches the National Integrated Computing Power Network (NICPN) to optimize and integrate computing resources.
2022-01
The 'East Data, West Computing' (EDWC) initiative is proposed to balance computing demand and resource availability.
2023-10
President Xi Jinping introduces the Global AI Governance Initiative.
2024-01
Daily consumption of AI tokens in China is recorded at 100 billion.
2026-03
Daily consumption of AI tokens in China surpasses 140 trillion, with Chinese AI models processing 4.12 trillion tokens on Open-Router, exceeding US models.
📎 Sources (18)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: 量子位 ↗