Multimodal large model inference costs down 90%
Next-gen inference architecture delivers efficiency leap
Executive Summary
Multimodal large model inference cost per run has dropped by over 90% within 6 months, moving from experimental stages to scaled deployment.
Triple technological breakthroughs in MoE architecture, quantized inference, and dedicated chips drive a dramatic cost reduction.
The first million-DAU agent applications are expected to launch in Q4 of 2026, marking the inflection point for AI app commercialization.
Trend Card
Surging market heat, with capital flooding in.
Historical growth curve
Why is growth increasing now?
Three technological breakthroughs converged in the same window: mature MoE sparse activation architectures reduced compute by an order of magnitude; INT4/INT8 quantization kept precision loss within acceptable limits; and mass production of inference-specific chips like Groq LPU delivered hardware-level acceleration. Together, they created a sharp drop in costs.
Four-Dimensional Drivers
Tech-driven
The MoE sparse activation architecture significantly reduces the computational load per inference.
INT4/INT8 quantization technology is mature with controllable precision loss.
Mass production of Groq LPU and other inference-specialized chips
Capital-driven
The large model sector continues to see active funding, with leading companies well-capitalized.
Capital-intensive attention focuses on startups in inference optimization
Policy-driven
National computing infrastructure policies drive supply of inference compute capacity
Open-source model policies encourage lowering technical barriers.
Market-driven
Enterprise AI adoption is surging, driving extreme sensitivity to inference costs.
Intensifying open-source model competition drives efficiency optimization
Related Companies, Products, Projects, and Capital
Impact Analysis
Impact on China
China is among the global leaders in large model inference optimization. Companies such as DeepSeek and Alibaba Cloud have achieved breakthroughs in MoE architectures and quantization technologies, positioning them to dominate the global race for reduced inference costs.
Key Provinces and Cities
Beijing: Hub of Large Model Enterprises with Strongest Inference Demand
Shanghai: Complete AI Chip Industry Chain with Prominent Inference Hardware Advantages
Shenzhen: Vibrant application-layer innovation with high sensitivity to inference costs.
Hangzhou: Leading in cloud infrastructure, mature ecosystem for inference services
Impact on Industry
AI SaaS: Lower inference costs directly boost gross margin.
AI Agent: Lower Cost Barriers Drive Mass Adoption of Agent Applications
Smart Customer Service: Inference Costs Are No Longer a Deployment Bottleneck
Content Generation: AIGC scaling costs significantly reduced
Business Opportunities
Inference-as-a-Service (RaaS): Cost optimization solutions for enterprise inference.
Edge-Cloud Collaborative Inference: A hybrid architecture service combining edge and cloud inference.
Vertical Industry Inference Optimization: Acceleration solutions tailored for industry-specific models.
Inference Monitoring and Scheduling Platform: Optimize inference resource allocation for your business
Risks and Uncertainties
Quantization accuracy may still produce hallucinations or biases on specific tasks.
Declining inference costs may accelerate the proliferation of low-quality AI content.
Overly concentrated hardware supply chain poses geopolitical risks.
Data Sources and Methodology
Research Methodology
Cross-validated reasoning cost trends using SemiAnalysis hardware benchmarks, DeepSeek's official technical report, third-party API pricing data, and interviews with 200+ enterprises conducted by Jingzhi Research Institute.
Data Source
Joy Ask
Industry Intelligent Q&A Based on the JoyHIAI Database
Trend Stage:Peak Period
Surging market heat, with capital flooding in.
Technology Trends
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