🚀 GQLSA v1.0.0 — Officially Released!

#ai #machinelearning #performance #softwaredevelopment

Grouped-Query Latent Sparse Attention

A hardware-native attention mechanism built on one principle:

"All tokens don't need compute. Compute only where it matters."

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📊 Performance:

⚡ 3.8× faster than Multi-Head Attention
💾 2.2× less memory at T=4096
📦 16× smaller KV cache (2 KB vs 32 KB)
🎯 Quality on par with dense attention
🔒 Verified causal correctness — 11/11 tests passed

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🔬 The Three Techniques:

1️⃣ Latent Compression
→ Reduces KV from 4096 to 512 dims

2️⃣ Grouped-Query Sharing
→ 4 KV groups across 32 query heads

3️⃣ Block-Sparse Selection
→ 192 tokens per query instead of all T

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💡 Key Innovation:

Unlike previous sparse attention methods that use slow Python loops, GQLSA formulates everything as a single batched matrix multiplication — fully utilizing GPU tensor cores.

This is why theoretical O(T) complexity translates to real 3.8× speedup.

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📜 License: CC BY-NC-SA 4.0

✅ Academic research
✅ Education
✅ Personal use
❌ Commercial — requires permission

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📄 Research Paper: https://doi.org/10.5281/zenodo.22658037

💻 GitHub: https://github.com/fardinsabid/gqlsa

🏷️ Release v1.0.0: https://github.com/fardinsabid/gqlsa/releases/tag/v1.0.0