Star Attention ; Supercharging LLM Inference with Speed & Accuracy 🚀✨

🚀✨ Excited to share an innovative leap in LLM efficiency! 🌟 Star Attention, a groundbreaking two-phase attention mechanism designed to supercharge inference for large language models on long sequences. Here’s the magic:

1️⃣ Phase 1: Blockwise-local attention ensures speed and scalability.
2️⃣ Phase 2: Sequence-global attention keeps the accuracy intact.

The results? ⚡ Up to 11x faster inference times while maintaining a stellar 95-100% accuracy. 🌟

Through experiments across various LLMs and benchmarks, Star Attention by NVIDIA shines in balancing speed 🏃‍♂️ and accuracy 🎯. Plus, it dives deep into optimizing block size and anchor block design for the perfect trade-off. 💡

Opening doors for optimizing the anchor block mechanism and enhancing performance on more complex long-context tasks—ideal for those looking to take the next step in this journey.

Paper


#AI #Innovation #Efficiency #StarAttention #LLM #Nvidia #Research




Enjoy Reading This Article?

Here are some more articles you might like to read next:

  • Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra
  • Displaying External Posts on Your al-folio Blog
  • AlphaGo Moment for Model Architecture Discovery ; The Rise of Autonomous AI Scientists 🤖🚀
  • Reinforcement pre-training - baking the cherry into the cake
  • Group Sequence Policy Optimization (GSPO); A Smarter Approach to RL for LLMs and MoE Models