Sparse Attention Heatmap

Dense vs. Sparse Attention

DeepSeek Sparse Attention (DSA): Move the sparsity slider and compare Dense Attention (chaotic, memory-intensive) with Sparse Attention (structured, 70% less memory).

Sparse Attention computes only relevant token pairs instead of the full n×n matrix. Dense Semantic Attention (DSA) uses ML to identify the most important connections — with 70% less memory at minimal quality loss.

📖 Learning Context

🎯 Learning Objectives

Understand dense vs. sparse attention patterns
Recognize sparsity patterns (local, diagonal, semantic)
Know quality trade-offs

🧭 Context

Step 4/5 in Chapter 2 "Modern Architecture Variants"

After Flash Attention: Algorithmic optimization for extreme context lengths (200K+). DSA makes 1M+ token contexts practical.

💡 Why It Matters

DSA is production-ready in Claude 4.5 for 200K+ contexts. Selects only ~10-20% of token pairs, without measurable quality loss for most tasks.

🔑 Key Takeaways

Sparse = selective: Only relevant token pairs are computed
Heatmap: Shows activation patterns — local + semantic
10-20% is enough: For most tasks without quality loss

🎚️ Sparsity Level

50%

Sparsity

Speed

1.5x

Memory

65%

Accuracy

99.8%

Inference Cost

-30%

💡 DeepSeek Sparse Attention (DSA) Mechanics

• Lightning Indexer: Computes relevance score for each token
• Top-K Selection: Selects only the most relevant tokens (based on sparsity level)
• Sparse Attention: Computes attention only on selected tokens
• Result: 60% lower costs, 3.5x faster, no accuracy regression

⚡

Speed Boost

Sparse Attention can be up to 3.5x faster than Dense Attention. Perfect for long sequences (128K+).

💾

Memory Savings

With DSA you need 70% less memory. This makes 1M+ context windows practically possible.

🎯

Smart Selection

The Lightning Indexer learns which tokens are relevant. No regression in accuracy — just efficiency!

🚀

Scalability

DSA enables true scaling to very long sequences, while Dense Attention immediately becomes a bottleneck.