Chapter 8

Architecture comparison and selection decision

Through multi-dimensional comparative analysis, master the decision-making framework for architecture selection and make the right architecture choice based on task type, resource constraints, and application scenarios.

Architecture comparison table

Compare the characteristics and applicable scenarios of different architectures from multiple perspectives.

Transformer

Computational complexityO(n²)
Long-sequence capabilityMedium (limited by context window)
Training efficiencyHigh (parallel training)
Inference efficiencyMedium (autoregressive generation)
Memory consumptionHigh (attention matrix)
Applicable scenariosGeneral-purpose tasks, short to medium sequences, multimodal
Ecosystem maturityVery high

Mamba (SSM)

Computational complexityO(n)
Long-sequence capabilityStrong (can handle 1M+ tokens)
Training efficiencyHigh (parallel scanning)
Inference efficiencyHigh (linear complexity)
Memory consumptionLow (state compression)
Applicable scenariosUltra-long sequences, real-time applications, resource-constrained
Ecosystem maturityMedium (rapidly developing)

MoE

Computational complexityO(n²) (but sparse activation)
Long-sequence capabilityMedium (limited by infrastructure constraints)
Training efficiencyHigh (sparse activation)
Inference efficiencyHigh (activate only some experts)
Memory consumptionModerate (few active parameters)
Applicable scenariosUltra-large-scale models, multi-domain applications, cost optimization
Ecosystem maturityHigh (GPT-4, Mixtral, etc.)

RAG

Computational complexityRetrieval + generation (retrieval O(log n))
Long-sequence capabilityStrong (expanded through retrieval)
Training efficiencyHigh (no fine-tuning required)
Inference efficiencyMedium (retrieval + generation latency)
Memory consumptionMedium (vector database)
Applicable scenariosKnowledge Q&A, enterprise applications, specialized domains
Ecosystem maturityHigh (mainstream for enterprise applications)

Selection decision framework

Choose the architecture based on task type, resource constraints, and application requirements.

Choose by task type

Text generation (short to medium length)

Recommended: Transformer (GPT series)

Reason: mature ecosystem, excellent performance, rich tooling

Long document analysis (100K+ tokens)

Recommended: Mamba or RAG

Reason: Mamba's linear complexity, RAG expands context through retrieval

Knowledge Q&A

Recommended：RAG

Reasons: interpretability, knowledge updates, reduced hallucinations

Multimodal tasks

Recommended：Transformer（GPT-4V、Gemini）

Reason: mature multimodal capabilities, unified architecture

Select according to resource constraints

Resources are abundant (ample GPU, ample memory)

Recommended: Transformer or MoE

Reason: Resources can be fully utilized to achieve optimal performance

Resource-constrained (edge devices, mobile devices)

Recommended: Mamba or small-scale Transformer

Reason: high memory and computational efficiency

Cost-sensitive (requires controlling inference costs)

Recommended: MoE or RAG

Reason: MoE uses sparse activation, and RAG does not require fine-tuning

Hybrid architecture strategy

In practical applications, multiple architectures can be combined to leverage their respective strengths.

Transformer + RAG

The most common hybrid architecture:

  • • Architecture: Use Transformer as the generative model, with RAG providing knowledge augmentation
  • • Advantages: combine the powerful capabilities of Transformers with RAG’s knowledge update capabilities
  • • App: enterprise knowledge base, domain-specific Q&A
  • • Case: ChatGPT + plugins, Claude + document retrieval

MoE + RAG

  • • Architecture: Use an MoE model as the generative model, with RAG providing knowledge augmentation
  • • Advantages: Ultra-large model + knowledge enhancement, balancing performance and cost
  • • App: Large-scale enterprise applications, multi-domain knowledge systems

Mamba + RAG

  • • Architecture: use Mamba to handle long sequences, and RAG to provide knowledge augmentation
  • • Advantages: Long-sequence processing + knowledge enhancement, suitable for long-document analysis
  • • App: Long-document Q&A, codebase analysis

Practical case study

Understand the practical application of architecture selection through real-world cases.

Case 1: Enterprise Knowledge Base Q&A

Requirements: an internal enterprise knowledge base Q&A system, required to answer employees’ questions about company policies, processes, and more
Select：RAG + Transformer（GPT-4）
Reason：
  • • Knowledge needs frequent updates (RAG advantage)
  • • Explainability is needed (an advantage of RAG)
  • • Need to reduce hallucinations (RAG advantage)
  • • High requirements for generation quality (Transformer advantage)
Effect: high accuracy, traceability, easy knowledge updates

Case 2: Analysis of a large codebase

Requirements: Analyze large codebases (one million lines of code) and understand code structure and dependencies
Select：Mamba
Reason：
  • • Sequence length is too long (1M+ tokens)
  • • Requires linear complexity (Mamba advantage)
  • • Memory constrained (Mamba advantage)
Effect: can process the entire codebase at once and understand the global structure

Case 3: Multilingual translation system

Requirements: Real-time translation supporting 100+ languages, requiring high quality and low latency
Select：MoE + Transformer
Reason：
  • • Multilingual capability required (MoE expert specialization)
  • • High-quality translation required (Transformer advantages)
  • • Need to control costs (MoE sparse activation)
Effect: High-quality translation, controllable cost, low latency

Learning outcomes

After completing this chapter, you will:

  • 1Master the decision-making framework for architecture selection and compare different architectures from multiple perspectives
  • 2Able to choose the appropriate architecture based on task type, resource constraints, and application requirements
  • 3Understand the design principles of hybrid architectures and be able to combine multiple architectures
  • 4Understand the application of architecture selection through real-world cases, and be able to analyze scenarios and make decisions
Previous chapter: Other emerging architecturesNext chapter: Future trends and outlook