Overview of the six core modes of large model implementation

01Why  do we need to redefine the big model implementation paradigm?

1.1 Industry pain points: the triple dilemma of large model implementation

1.1.1  Cost Black Hole: The Unsustainability of the Computing Power Arms Race

The training of a model with hundreds of billions of parameters requires thousands of GPUs to run in parallel, and the cost of a single training session exceeds tens of millions of dollars (for example, the training cost of GPT-4 is about 130 million dollars).

The deployment cost of SMEs is too high: A retail enterprise invested 2 million GPU resources to build its own customer service model, but eventually abandoned the project due to insufficient ROI .

1.1.2 Scenario fog: the gap between general models and vertical needs

The medical field requires diagnostic accuracy (e.g. IBM Watson’s misdiagnosis rate must be less than 0.1% ), while general models have an error rate of 15% in understanding professional terms.

Manufacturing quality inspection scenarios require millisecond-level responses, but the latency of large cloud models generally exceeds 200ms.

1.1.3  Technical puzzle: tool chain fragmentation and talent gap

Enterprises use an average of 3.2 AI frameworks ( TensorFlow/PyTorch , etc.), and technology stack integration takes up 40% of the project cycle

The global AI talent gap has reached 5 million, and engineers with large model fine-tuning capabilities have an annual salary of more than 1.5 million yuan

1.2  Paradigm Revolution: From Technological Breakthrough to System Reconstruction

1. 2.1  Methodology upgrade: six model matrix

Break through the single-point technology thinking and build a "scenario - data - computing power" trinity solution

Case: Tencent Cloud and Tongcheng Travel cooperated (intelligent customer service response speed increased by 80% , and labor costs decreased by 40% )

Hybrid deployment mode: core data localization + general capability cloud call

80% of simple consultations are diverted to the rule engine through intelligent routing

1.2.2 Technical architecture evolution

From "end-to-end big model" to "modular capability center"

Next-generation architecture features:

Dynamic Knowledge Distillation: Migrating Model Capabilities with Hundreds of Billions of Parameters to Lightweight Agents

Federated learning enhancement: Collaborative training of multi-institutional data (e.g., the accuracy of the medical alliance model increased by 23% )

1.2.3  Reconstruction of Business Value

The ROI cycle of traditional AI projects is 18-24 months, which can be shortened to 6-12 months by adapting to the new model.

Intelligent risk control project of a bank:

Old model: purchasing foreign models with an annual fee of 8 million and a false alarm rate of 5%

New model: self-built vertical model + RAG enhancement, annual cost reduced to 2 million, false alarm rate 0.3%

02In-  depth analysis of the six core modes

2.1  Mode 1 : MaaS (Model as a Service)

2.1.1  Technical features

Modular architecture: Disassemble the large model into sub-modules such as NLP and CV , and dynamically call them through the API gateway (such as the combination of GPT-4+DALL·E )

Elastic expansion: Automatically expand computing nodes to cope with traffic peaks ( QPS increased from 1k to 50k during a certain e-commerce promotion )

Multi-model orchestration: Supports model concatenation (such as BERT extracting intent and then GPT-4 generating responses)

2.1.2  Applicable Scenarios

Small and medium-sized enterprises can quickly access AI capabilities ( API call cost <$0.01/ time)

Multimodal application development (image and text generation, voice interaction, etc.)

2.1.3  Typical Cases

Intelligent customer service of a cross-border e-commerce company: Call GPT-4 to generate replies + Whisper to transcribe speech, reducing labor costs by 70%

AI editing on a short video platform：Stable Diffusion generates cover + FFmpeg automatic editing, processing more than 100,000 videos per day

2.2  Mode 2 : Vertical Model

2.2.1  Technical characteristics

Domain Knowledge Distillation: Migrate general model capabilities to vertical fields (e.g. compress the number of parameters of the medical version of GPT-3.5 to 1/10 )

Small-sample learning：Only a hundred labeled data are needed for fine-tuning (the accuracy rate of a legal document model is 91% )

Security hardening: Protect sensitive data through differential privacy ( ε value < 2 )

2.2.2  Applicable scenarios

Fields requiring high precision (financial risk control, legal documents)

Data privacy sensitive scenarios (government affairs, medical care)

2.2.3  Typical Cases

Ant Group Bailing Large Model: The accuracy of medical consultation is 97% , and the misdiagnosis rate is reduced by 82% compared with the general model

A bank's smart investment advisor: Generate personalized financial solutions based on customer profiles, increase AUM by 23%

2.3  Mode 3 : Intelligent Agent Mini Program

2.3.1  Technical characteristics

Lightweight architecture：Model parameter quantity <1B , response delay <200ms (WeChat plug-in form)

Scenario-based knowledge base: Embedded domain terminology library (such as travel guides containing 5000+ attractions information)

Multimodal interaction: Support mixed input of voice, image and text

2.3.2  Applicable scenarios

C -end high-frequency scenarios (travel, workplace skills)

Internal enterprise tools (meeting minutes, approval process)

2.3.3  Typical Cases

Feishu Intelligent Assistant: Meeting recordings automatically generate to-do items with an accuracy rate of 92%

Smart counter of a bank: Automatically recommend financial products to customers after face recognition, increasing conversion rate by 15%

2.4  Mode 4 : Embodied Intelligence

2.4.1  Technical characteristics

Multimodal perception fusion：Vision + touch + motion control joint training (such as the robot grasping objects with a success rate of 99% )

Real-time decision engine：Case-side inference latency <50ms (Tesla Optimus factory inspection system)

Modeling the physical world: Building a digital twin of the environment ( AGV navigation error at a port < 2cm )

2.4.2 Applicable Scenarios

Intelligent manufacturing (equipment inspection, assembly)

Logistics warehousing (sorting, route planning)

2.4.3 Typical Cases

Quality inspection robot in an automobile factory: Visual recognition + robotic arm operation, defect detection rate 99.3%

Logistics robot in a hospital: Autonomous obstacle avoidance + material distribution, with an average daily transportation volume of more than 2,000 times

2.5 Model 5 : AI -based productivity tools

2.5.1  Technical characteristics

Domain Enhancement Training: Inject professional data (such as legal texts, code bases) into the general corpus

Workflow Integration: Deep integration with existing software (such as VS Code plug-in form)

Low-code extensions: Business personnel configure AI capabilities through a visual interface

2.5.2  Applicable Scenarios

B -side productivity scenarios (code development, data analysis)

Creative design (copywriting, image editing)

2.5.3  Typical Cases

GitHub Copilot：Developer coding efficiency increased by 55% and code defect rate decreased by 30%

AIGC tool of a design company：Midjourney generates the first draft + manual optimization, shortening the project cycle by 40%

2.6  Mode 6 : Ecosystem Co-construction

2.6.1  Technical characteristics

Open source community driven: Attract developers to contribute models / data (such as Hugging Face model library)

Federated Learning Framework：Multi-institution collaborative training (the accuracy of the medical alliance model increased by 23% )

Model Trading Market：Provide one-stop service for model evaluation / trading / deployment

2.6.2  Applicable scenarios

Technology ecosystem builders (cloud vendors, open source foundations)

Long-tail scenario solutions (niche industry model)

2.6.3  Typical Cases

Meta Llama Open Source Model: Downloads exceeded 2 million times, and derivative applications exceeded 50,000

A smart city alliance：12 companies shared traffic flow prediction model, and the congestion index dropped by 18%

03   Technical Architecture Comparison

3.1 Six modes and four quadrants analysis:

3.2 Key indicators:

04Enterprise  decision tree: How to choose the adaptation model?

Decision logic framework

Based on the three core dimensions of enterprise scale, data characteristics, and real-time requirements, a dynamic decision-making model is built. The optimal model is locked through the "three-step progressive method" and matched with typical industry scenarios and technical parameters.

Step 1: Assess the size and resource endowment of the enterprise

1.1  Startups (Team size < 50 people, annual revenue < 50 million)

Core Features: Resources are limited, scenario verification is prioritized

Adaptation mode:

MaaS (Model as a Service): Directly call cloud APIs (such as Azure Cognitive Services ) to avoid building your own computing infrastructure

Intelligent Agent Mini Program：Lightweight Agent embedded in existing tools (such as enterprise WeChat plug-in)

Typical Cases:

A cross-border e-commerce company: Using MaaS to call GPT-4 to generate multilingual product descriptions, it was launched in 3 weeks and labor costs were reduced by 70%.

A new consumer brand: WeChat applet has built-in AI customer service, handling more than 2,000 inquiries per day

Technical Parameters:

Cost per API call < $0.01

Deployment cycle < 1 week

1.2  Medium-sized enterprises ( 50-500 people, annual revenue of 50 million to 500 million)

Core Features：High business complexity, need to balance efficiency and cost

Adaptation mode:

Vertical Model + RAG: Inject industry data (such as legal provisions / financial reports) based on the general model

Agent combination: Multi- agent collaboration (such as financial audit agent + compliance review agent )

Typical Cases:

A city commercial bank: Self-developed vertical anti-money laundering model (100,000 training data ) , the false alarm rate dropped from 5% to 0.3%

A chain retail enterprise: Deployed intelligent inventory forecasting agent , out-of-stock rate decreased by 40%

Technical Parameters:

Model fine-tuning costs about $ 500,000 per year

Inference delay < 300ms

1.3  Group companies (Team > 500 people, annual revenue > 500 million)

Core Features: Multi-format collaboration requires the construction of long-term technical barriers

Adaptation mode:

Embodied Intelligence: Physical world interaction systems (such as industrial quality inspection robots)

Ecological co-construction: Open source model + developer community (such as medical alliance model)

Typical Cases:

A car company: Self-developed Optimus factory inspection robot (defect detection rate 99.3% ), replacing 80% of manual inspections

A cloud computing vendor: Launched MaaS platform, attracted 500+ corporate customers, and API calls exceeded 2 billion times per month

Technical Parameters:

Hardware investment ＞$5 million / project

Model iteration cycle 3-6 months

Step 2: Analyze data characteristics and governance capabilities

2.1  Data volume dimension

2.2  Data Quality Dimensions

Mainly structured data(such as financial transaction records) →  Give priority to fine-tuning vertical models

Mainly unstructured data(such as medical images / text) →  Requires RAG+ knowledge graph enhancement

Data privacy sensitive(such as genetic data) →  Federated learning architecture must be adopted

2.3  Data Governance Capabilities

Mature Enterprises(Existing data center) →  Can build vertical model by yourself

Startups(Data dispersion) →  Rely on MaaS or intelligent agent applets

Step 3: Identify real-time requirements and technical constraints

3.1  Delay-sensitive scenarios (< 100ms )

Typical industries: Industrial quality inspection, autonomous driving, high-frequency trading

Adaptation mode:

Embodied Intelligence: Edge computing node deployment (such as factory quality inspection robots)

Model lightweight：Knowledge distillation to Agents with less than 1B parameters

Technical Parameters:

Client-side inference latency < 50ms

Hardware requirements: Edge devices with computing power ≥ 16TOPS

3.2  Delay-tolerant scenarios (＞500ms )

Typical industries：Content creation, strategic decision support

Adaptation mode:

Cloud hybrid architecture：Core data localization + general computing cloudification

MaaS Services: Call the cloud-based model with hundreds of billions of parameters on demand

Technical Parameters:

Average response time 200-500ms

Cost savings of 30%-50%

3.3  Burst Traffic Scenario

Solution:

Elastic scaling architecture：MaaS automatically expands computing nodes (e.g. QPS from 1k to 50k during e-commerce promotions )

Asynchronous processing mechanism: Intelligent agent applet caches high-frequency requests

Decision-making tool: Model fit index matrix

05Future  Evolution: Integration Trend of Six Models

1.  Cross-model innovation: from single capability to system-level solutions

Technology Integration Path

Productivity Tools + Intelligent Agents： Microsoft Copilot integrates GPT-4 's generation capabilities with the Azure agent platform to achieve end-to-end automation from web page processing to supply chain management. For example, after the user enters a natural language instruction, the system automatically calls the knowledge base to generate a draft report (productivity tool) and triggers the supply chain agent to adjust inventory (process automation), improving overall efficiency by 300% .

Multimodality + Embodied Intelligence： GPT-4V 's image and text understanding capabilities are combined with industrial robots to achieve a closed loop of visual inspection - decision - execution. Tesla's Optimus factory robot parses product images through a multimodal framework, generates quality inspection reports in real time and triggers robotic arm repair actions, reducing the defect missed detection rate to 0.2% .

Data Validation

2.  Technology stack convergence: from fragmentation to standardized architecture

Architecture Unification Trend

Multimodal Unified Framework：GPT-4V 's Transformer-ViT hybrid architecture has achieved joint encoding of text / image / audio, and the parameter sharing rate has been increased to 75% . Meta 's open source ImageBind framework supports 6 modal alignments, and the training efficiency is 40% higher than traditional methods .

Lightweight deployment standards：DeepSeek-R1 pioneered the UltraMem sparse architecture, with an inference speed of 20 tokens/ second and a 60% improvement in hardware compatibility . China Academy of Information and Communications Technology predicts that by 2026, 90 % of inference scenarios will use lightweight models with <10B parameters.

Development paradigm change

#Traditional  Multimodal Development ( 2024 )

text_processor = BertTokenizer()

image_processor = ResNet50()

fusion_layer = CustomAttention()

#Unified  Architecture Development ( 2026 )

multi_modal = UnifiedTransformer( 

 modalities=["text", "image", "audio"],  

 shared_encoder="ViT-Huge"  

) 

3.  Industry base model: from vertical cultivation to ecological co-construction

Technological breakthrough direction

Cross-domain generalization capability: The universal base model expected to appear in 2026 will have:

Dynamic Knowledge Graph：Automatically extract entity relationships in fields such as medical / financial / legal (accuracy > 92% )

Adaptive Inference Engine: Switch computing mode according to task type (such as enabling sparse attention for mathematical reasoning)

Federated Learning Enhancement: Supports collaborative training of more than 100 institutions, and data does not leave the local area (for example, the misdiagnosis rate of the medical alliance model has dropped by 23% )

Reconstruction of business ecosystem

Open source community driven：Meta Llama 3 attracted 500,000 developers through open source strategy , and derived over 100,000 applications , forcing the closed-source model to upgrade its functions.

The rise of industry alliances：China's " Spark " Large Model Alliance has united 30 institutions, covering the fields of government affairs / education / medical care, and the model reuse rate has increased to 75% .

4. Prediction of the technology turning point in 2026

Key Metrics

Conclusion: Choice is more important than hard work

Recommended actions

1.  Prioritize pilot projects in scenarios with high fault tolerance and good data foundation

Scene screening criteria:

Fault Tolerance: Choose scenarios that have little impact on business continuity (such as optimizing customer service skills rather than core transaction decisions)

Data foundation：Ensure that the scenario data integrity rate is > 80% and the annotation quality meets the standards (e.g., financial anti-money laundering scenarios require more than 100,000 annotated samples)

Quick Verification：Use the MVP (minimum viable product) model and complete effect verification within 3 weeks (such as the first month resolution rate of intelligent customer service > 75% )

2.  Establish a three-dimensional evaluation system of technology , business and finance

Implementation Path:

Quarterly Review: Dynamically adjust resource investment based on evaluation results (e.g. a retail company shifts AI budget from customer service to supply chain optimization)

Risk Hedging: Set 10%-15% of the budget to explore emerging models (such as the application of federated learning in cross-institutional medical data)