CHAPTER 1.9 · MODEL ARCHITECTURES & VARIANTS

Architecture Comparison: Dense vs MoE vs Hybrid

Three fundamental architecture paradigms and their trade-offs: Simplicity vs Efficiency vs Innovation

Model Architecture Comparison concludes Chapter 1: With knowledge of all Transformer components, we can now compare the three major architecture paradigms. Dense Transformer (GPT-4, Claude), Mixture of Experts (Mixtral, DeepSeek), and Hybrid models (Mamba) each have clear strengths and trade-offs.

📖 Learning Context ▼
🎯

Learning Objectives

  • Understand the trade-offs between Dense, MoE, and Hybrid architectures
  • Know why Dense dominates production despite efficiency disadvantages
  • Be able to assess which architecture is optimal for which use case
🗺️

Context: Where are we?

Synthesis Chapter 1 Conclusion

After working through all Transformer components (Tokenization → Embedding → Position → Attention → Multi-Head → FFN → Residual/Norm → Block), we now have the overall perspective: How do real models vary these basic building blocks?

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Why it matters

The choice of architecture determines costs, latency, and deployment complexity. MoE can be 10× more efficient but requires complex load-balancing. Hybrid enables unlimited context, but reasoning quality is not yet at Dense level. Understanding these trade-offs helps with model selection.

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Key Takeaways

  • Dense: All parameters active, simple training, 100% production-ready
  • MoE: Only top-k experts active, scales parameters at same compute
  • Hybrid: O(n) instead of O(n²), theoretically unlimited context, but still research phase
🔴 Dense (Transformer Standard)
All parameters are active for every Token. Basis: Vaswani et al. Attention is All You Need.
Parameters
All active
Inference Speed
Slow (large)
Training
Simple, stable
Context
Up to ~200K
Models: GPT-4, Claude 3.5, Llama 3.1 405B
🟢 Sparse MoE (Mixture of Experts)
Only top-k experts active per token. Scales parameters at same compute.
Parameters
Most inactive
Inference Speed
Fast (top-k)
Training
More complex (Load Bal.)
Context
Standard
Models: Mixtral 8×7B, DeepSeek V3
🔵 Hybrid (Modern Innovations)
Combination of Attention + Linear RNNs or State-Machines. Reduces O(n²) complexity.
Parameters
Efficient
Inference Speed
Very fast
Training
New, researched
Context
Unlimited
Models: Mamba, Hydra, RWKV
Property Dense MoE Hybrid
Memory Complexity O(n) Attention, O(d²) FFN O(n) Router, O(d) Expert O(1) or O(n) depending on design
Trainability Simple, converges well Unstable, needs balancing Still being researched
Inference Latency O(n) with KV-Cache O(log n) Router + Top-k O(1) ideal
Long Context Flash Attention → 200K Same as Dense Unlimited possible
Deployment Standard, many optimizations Complex routing Not yet mainstream
Production-Readiness 100% proven 95% (Mixtral, DeepSeek) 50% (research)
📊
Dense Dominates Production
GPT-4, Claude, Llama are all dense. Simplicity in training & deployment beats efficiency gains of MoE. KV-Cache + Flash Attention is enough.
⚡
MoE = Efficiency Multiplier
Mixtral 8×7B: 13B active, but 47B total. Saves compute while keeping many parameters. But router overhead + load-balancing is complex.
🚀
Hybrid Frontier 2025+
Mamba, State Space Models: O(n) instead of O(n²). Theoretically unlimited context. But reasoning capabilities not yet at attention level.
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Choice Depends On:
Latency requirements: Hybrid. Accuracy priority: Dense. Cost efficiency: MoE. Unlimited context: Hybrid. Production: Dense.
📈
Scaling Laws Differ
Dense: Power-law with parameters. MoE: Sub-linear (router overhead). Hybrid: Unknown (not yet calibrated).
🔮
Future: Hybrid + Dense Mix
Probably not Hybrid alone, but Hybrid for long sequences, Dense for reasoning. Or Hybrid router chooses Dense blocks.