How does the sparsity level affect inference performance? Interactively explore how speed, memory, and accuracy change.
Sparsity Level Tradeoffs: More sparsity = faster and less memory, but potential quality loss. This demo shows the sweet spot: 50-60% sparsity for 3-4× speedup at <2% accuracy loss.
Step 4/5 in Chapter 2 "Modern Architecture Variants"
Practical configuration of Sparse Attention. Shows how hyperparameter choice affects production deployment.
80-90% sparsity is often possible without measurable quality loss for long contexts. For 1M+ token contexts recommended: 70% sparsity (6× speed, KV-Cache <10GB).
Speedup vs. Dense Attention
Memory relative to Dense
Relative to full network
Inference speed increases almost linearly with sparsity. At 80% sparsity, the model is ~6.5× faster, as only ~20% of attention operations are performed.
KV-Cache size decreases quadratically with sequence length. At 80% sparsity (128K sequence), you save 12.8 GB with mixed precision. This enables 1M+ token contexts.
The best balance between speed and accuracy is at 50-60% sparsity. Here you achieve 3-4× speedup with only <2% accuracy loss. Above that, the trade-off becomes less favorable.
At very high sparsity (>80%), accuracy drops quickly. Too many relevant tokens are ignored, especially for reasoning tasks. Models with strong redundancy tolerate higher sparsity.
Sparse Attention is not universal. DeepSeek V3 with DSA tolerates 60% sparsity, while GPT-4 tends to handle only 40%. Training with Sparse Attention is essential for good results.
For production: Choose 50% sparsity for standard workloads (3.5× speed, 99% accuracy). For long context (1M tokens) recommended: 70% sparsity (6× speed with KV-Cache <10GB).