LLMOps Architecture

System architecture design for the complete LLM training and optimization platform.

Table of contents

1. System Architecture
   1. Overall Architecture Diagram
   2. Component Architecture
      1. 1. Training Engine
      2. 2. Data Pipeline
      3. 3. Serving & Evaluation
   3. Technology Stack
2. Design Principles
   1. 1. Modularity
   2. 2. Scalability
   3. 3. Automation
   4. 4. Cost Efficiency
3. Deployment Architecture
   1. Development Environment
   2. Production Environment
4. Integration Points
   1. External Systems
   2. API Interfaces
5. Security & Privacy
   1. Data Protection
   2. Model Safety
6. Performance Considerations
   1. Training Performance
   2. Serving Performance
7. Future Enhancements

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System Architecture

Overall Architecture Diagram

The LLMOps system follows a modular architecture with clear separation between offline training and online optimization:

graph TB
    subgraph OfflineTraining["Offline Training Pipeline"]
        A[Domain Corpus] --> B[CPT Training]
        C[Instruction Dataset] --> D[SFT Training]
        B --> D
        D --> E[Base Chat Model]
    end
    
    subgraph OnlineOptimization["Online Feedback Loop"]
        E --> F[Model Serving]
        F --> G[A/B Testing]
        G --> H[User Feedback Collection]
        H --> I[Preference Dataset]
        I --> J[DPO Training]
        J --> K[Optimized Model]
        K --> F
    end
    
    subgraph DataGeneration["Data Generation"]
        L[Self-Instruct] --> C
        M[Human Annotation] --> I
        N[LLM Generation] --> I
    end
    
    subgraph Infrastructure["Infrastructure Layer"]
        O[LLaMA-Factory]
        P[Gradio WebUI]
        Q[Experiment Tracking]
        R[Model Registry]
    end

Component Architecture

1. Training Engine

Core Components:

• LLaMA-Factory Integration: Mature training framework with multi-backend support
• Configuration Management: YAML-based declarative training configs
• Resource Orchestration: Distributed training coordinator
• Checkpoint Management: Model versioning and storage

Key Features:

• Support for CPT, SFT, DPO training stages
• LoRA/QLoRA efficient fine-tuning
• Multi-GPU distributed training
• Automatic mixed precision (AMP)

2. Data Pipeline

Components:

• Data Loaders: Support for multiple data formats (JSONL, CSV, Parquet)
• Preprocessing: Tokenization, truncation, padding
• Data Generation: Self-Instruct for instruction generation
• Quality Control: Automatic filtering and validation

Workflow:

graph LR
    A[Raw Data] --> B[Format Conversion]
    B --> C[Quality Filter]
    C --> D[Tokenization]
    D --> E[Dataset Split]
    E --> F[Training Data]

3. Serving & Evaluation

Components:

• Model Server: Ollama/vLLM-based inference
• A/B Testing: Traffic splitting and experiment management
• Metrics Collection: User feedback, engagement metrics
• Evaluation Suite: Automated benchmarks

Technology Stack

Layer	Technology	Purpose
Training	LLaMA-Factory	Core training engine
Optimization	LoRA/QLoRA	Efficient fine-tuning
Serving	Ollama/vLLM	Model inference
UI	Gradio	Web interface
Tracking	TensorBoard	Experiment monitoring
Storage	HuggingFace Hub	Model registry

---

Design Principles

1. Modularity

Each component is independently replaceable:

• Training framework can be swapped (LLaMA-Factory → Axolotl)
• Serving backend is pluggable (Ollama → vLLM → TGI)
• Data generation can use different methods

2. Scalability

System designed for growth:

• Horizontal scaling for serving
• Distributed training support
• Incremental model updates (LoRA)

3. Automation

Minimize manual intervention:

• Automated data generation (Self-Instruct)
• Configuration-driven training
• Automatic checkpoint management
• Continuous deployment pipeline

4. Cost Efficiency

Optimize for resource usage:

• LoRA for efficient fine-tuning (10x less memory)
• Quantization for deployment (INT4/INT8)
• Incremental updates instead of full retraining

---

Deployment Architecture

Development Environment

├── Data Generation
│   └── Self-Instruct scripts
├── Training Node
│   ├── LLaMA-Factory
│   └── GPU resources
├── Evaluation Node
│   └── Benchmark suite
└── Web UI
    └── Gradio interface

Production Environment

├── Data Pipeline
│   ├── User feedback collector
│   └── Preference dataset builder
├── Training Cluster
│   ├── Master node (orchestration)
│   └── Worker nodes (GPU training)
├── Serving Cluster
│   ├── Load balancer
│   ├── Model servers (A/B variants)
│   └── Monitoring
└── Storage
    ├── Model registry
    └── Training data

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Integration Points

External Systems

1. Data Sources
   • User interaction logs
   • Human annotation platforms
   • External corpora
2. Model Registry
   • HuggingFace Hub
   • Local model storage
   • Version control
3. Monitoring
   • TensorBoard for training metrics
   • Prometheus for serving metrics
   • Custom dashboards

API Interfaces

# Training API
POST /train/cpt
POST /train/sft
POST /train/dpo

# Serving API
POST /predict
GET /models
POST /feedback

# Management API
GET /experiments
GET /metrics
POST /deploy

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Security & Privacy

Data Protection

• Local deployment option for sensitive data
• Data anonymization before training
• Access control for model checkpoints

Model Safety

• Alignment training through DPO
• Content filtering in outputs
• Bias detection in training data

---

Performance Considerations

Training Performance

Optimization	Benefit
LoRA	10x memory reduction
Gradient Checkpointing	50% memory saving
Flash Attention	3x speedup
Multi-GPU	Linear scaling

Serving Performance

Technique	Latency	Throughput
Quantization (INT4)	50ms	100 req/s
Batch Inference	30ms	500 req/s
vLLM (PagedAttention)	20ms	1000 req/s

---

Future Enhancements

1. Advanced Training
   • Multi-task learning
   • Federated learning
   • RLHF integration
2. Scalability
   • Kubernetes deployment
   • Auto-scaling
   • Global distribution
3. Intelligence
   • Automatic hyperparameter tuning
   • Dynamic data selection
   • Online learning