Computing Power Matching and Reselling Platform (Token Factory Model)

📌 Background

In 2026, China's computing power infrastructure is projected to reach a scale of 4 trillion RMB, with high-end computing power continuing to rise in price. Traditional hardware leasing models are transitioning toward 'computing power matching' billed by tokens. Leading enterprises are exploring the new 'Token factory' model, where computing power can be scheduled and distributed like water and electricity. This aggregates fragmented computing power demands, enabling participation in the redistribution of the computing power economy without holding heavy assets, and becoming a new market hotspot.

👤 Target Customers

1. AI startups and developers needing elastic GPU resources; 2. Intelligent computing centers, IDCs, and 'retail' holders of high-performance graphics cards with idle computing power; 3. Enterprise clients seeking to pay directly based on results (token consumption).

💰 Revenue Streams

1. Computing power matching spread: Lock in upstream idle computing power via wholesale agreements, and resell to downstream clients at a markup based on token consumption or GPU hours, earning a 10%-30% commission. 2. Computing power financialization revenue: Prepay capital to computing power providers to lock in low-priced packages, then retail them at market prices for higher margins, capturing cash flow premiums. 3. Value-added service fees: Provide SaaS subscriptions or project fees for multi-platform computing power price comparison, cost estimation tools, and cross-regional computing power trial scheduling.

🧮 Cost Structure

1. Computing power procurement or leasing prepayments, which constitute the absolute majority of costs; 2. R&D and hardware costs for hybrid cloud scheduling systems; 3. Scalable customer acquisition and sales team costs targeting small and medium-sized B-side clients; 4. Inventory impairment risks caused by fluctuations in computing power prices.

🛡️ Moat

Deep integration of scheduling capabilities for multi-source computing power, building an algorithmic firewall with 'multi-tenant, scalable token pricing'. First-mover brand effects attract both supply and demand sides to form a network effect, bypassing the tier-1 wholesale blockades of giant cloud vendors.

🔑 Keys to Success

• Build absolute scale on both supply and demand sides to rapidly form an 'absolute match' network effect between buyers and sellers.
• Establish real-time computing power price crawlers and intelligent scheduling algorithms to compress matching cost differences and counter price fluctuations via technology.
• Targeting the AI entrepreneurial community, acquire customers using strategies such as 'token incentive programs' and 'free lunch computing power packages'.

⚠️ Risks

• Hardware upgrades cause severe depreciation of older GPU computing power, resulting in immediate losses for large inventories of legacy graphics cards.
• Computing power providers bypass the platform, connecting supply and demand directly.
• Self-sufficient major cloud enterprises block third-party distribution channels, and policy interventions compress scheduling revenue.

🏢 Cases

• Wuxiang Yungugu Intelligent Computing Center, capable of producing 200 billion tokens per hour, became the nation's first commercialized token factory, leading the time-based and volume-based agency distribution model.
• SiliconFlow (Silicon Flow), a major token model player originating from CSDN, launched a token mall in 2024, converting computing power into standardized commodities for distribution to small and medium-sized developers.

📊 SWOT Analysis