CHAPTER 2.5 · NATIVE MULTIMODAL

Late vs. Early Fusion

Llama 4 Architecture: Compare Sequential Pipeline (Late Fusion) with Joint Processing (Early Fusion) for better Cross-Modal Reasoning.

Early Fusion integrates vision tokens directly into the transformer layers instead of as a separate encoder. This enables native cross-modal attention from the start — the key to true visual reasoning.

📖 Learning Context
🎯 Learning Objectives
  • Distinguish between late and early fusion
  • Understand cross-modal attention
  • Know architecture trade-offs
🧭 Context

Step 5/5 in Chapter 2 "Modern Architecture Variants"

Multimodal extension of the Transformer. Early Fusion is the architectural paradigm shift for Vision+Language.

💡 Why It Matters

Llama 4, Gemini 3 use Early Fusion. GPT-4V still used Late Fusion. The switch brings +20-30% better accuracy on Visual QA and enables true reasoning about images.

🔑 Key Takeaways
  • Early = native integration: Vision tokens are treated like text tokens
  • Shared space: All modalities share the same embedding space
  • Cross-Modal Attention: Direct connections between vision and text
🔄 Architecture Comparison
❌ Late Fusion (Sequential)
Traditional approach: Vision and text are processed separately, then combined at the end.
✅ Early Fusion (Joint Processing)
Llama 4 approach: Vision and text are processed together from the start for better reasoning.
Vision Tokens
Text Tokens
Attention
Aspect Late Fusion (Sequential) Early Fusion (Llama 4)
Pipeline Vision → Dense Vector → Text → LLM Vision + Text → Interleaved → Unified Transformer
Cross-Modal Reasoning Limited (only at the end) Throughout all layers
Encoder Separate Vision/Text Encoder MetaCLIP-based Vision → Token Space
Context 2K Vision Tokens + Text Million+ Token Context (joint)
Information Loss High (bottleneck at merge) Minimal (direct token representation)
Reasoning Quality ⭐⭐⭐ ⭐⭐⭐⭐⭐
Compute Efficiency Higher (separate processing) Unified Framework (optimized)
💡 MetaCLIP Vision Encoder (Llama 4)

• Base: OpenAI CLIP with improvements
• Output: Tokens in the same vocabulary space as text
• Advantage: Vision and text can directly interact with each other
🔀
Joint Processing
Early Fusion enables true cross-modal attention from the start. Every transformer layer can process vision and text simultaneously.
🎯
Better Reasoning
With Early Fusion, models can recognize subtle relationships between images and text, not just surface-level features.
📈
Scalability
Llama 4 with Early Fusion supports million-token context windows with video + audio + text simultaneously.
🚀
Future-Ready
Early Fusion is the future standard for Multimodal LLMs. All new models (Llama 4, Gemini 3, Qwen3-VL) follow this pattern.