Thanks for sharing our newest work @_akhaliq ! Classic algorithms like K-Means deserve to be revisited in the era of massive datasets and GPUs. Flash-KMeans rethinks the algorithm from a systems perspective to make exact K-Means fast and memory-efficient on modern hardware.

𝗞-𝗺𝗲𝗮𝗻𝘀 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗳𝗮𝘀𝘁 𝗼𝗻 𝗚𝗣𝗨𝘀 𝗶𝘀𝗻’𝘁. That’s why we built Flash-KMeans — an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves 30x speedup over cuML and 200x speedup over FAISS — with the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. A classic algorithm — redesigned for modern GPUs. Paper: Code:

🚀 Introducing Sparse VideoGen2 (SVG2) — Pareto-frontier video generation acceleration with semantic-aware sparse attention! 🏆Spotlight paper accepted by #NeurIPS2025 ✅ Training-free & plug-and-play ✅ Up to 2.5× faster on HunyuanVideo, 1.9× faster on Wan 2.1 ✅ SOTA quality preserved (no artifacts, no flickering, no distortion) 📚Paper: 💻Code: 🌐Website: ⚡Attention Kernel: Joint work with: @randwalk0 @ylzhao_dreamer @lmxyy1999 Jintao Zhang @hancai_hm @syn7xavier @xiuyu_l @Chenfeng_X @ZiqiPeng @jianfei_chen @songhan_mit @KurtKeutzer @istoica05 See details below ⬇️ (1/6) @NeurIPSConf #NeurIPS #VideoGeneration #GenerativeAI #MachineLearning #EfficientAI #OpenSource