谷歌 DeepMind 发布 TurboQuant：在毫秒级在线场景中，实现逼近香农下界的极致向量压缩
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谷歌 DeepMind 发布 TurboQuant：在毫秒级在线场景中，实现逼近香农下界的极致向量压缩
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Google 研究團隊發表 TurboQuant，這是一種 AI 壓縮技術，能用極致方式大幅減少大型語言模型的記憶體用量，同時完全不損失準確度。AI 模型處理文字、圖片時，會使用大量「高維度向量」，這些向量雖然強大，但非常吃記憶體，尤其在「關鍵-值快取」（KV Cache）中會造成嚴重瓶頸。傳統壓縮方法雖然能縮小尺寸，卻會產生額外記憶體負擔。TurboQuant 巧妙解決這個問題，它結合兩大創新技術：1. PolarQuant：把向量從一般座標轉成「極座標」（像用距離 + 角度來描述位置），讓壓縮更有效率，完全消除傳統方法的額外記憶體開銷。2. QJL（Quantized Johnson-Lindenstrauss）：只用 1 個位元就能修正剩餘的微小誤差，確保計算結果精準無誤，而且完全沒有額外負擔。實際測試顯示，TurboQuant 可以把 KV 快取壓縮到僅 3 位元，記憶體用量至少減少 6 倍，在 LongBench、Needle In A Haystack 等長上下文任務中，模型表現幾乎零損失。在 H100 GPU 上，注意力計算速度還能提升最高 8 倍。用在向量搜尋時，準確率
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Someone from uWaterloo Computer Engineering implemented Google Research’s TurboQuant as a full CUDA-native compression engine on Blackwell B200 5x KV cache compression on Qwen 2.5-1.5B, near-lossless attention scores, live generation from compressed memory, and 5 custom cuTile kernels with fused attention, online softmax, and on-chip decompression.Respect @anirudhbv_ce 🙌
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