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9:34
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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1 month ago
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OpenAI危險了!Google祭出「Turboquant」黑科技,AI推理成本狂降80% 🚀 徹底終結記憶斷層!
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🔥 Why TileKernels has 3 devs switching right now #Shorts
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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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Google Turbo Quant running Locally in Atomic ChatMacBook Air M4 16 GBModel: QWEN3.5-9BContext window: 100000Summarising 50000 words in just seconds..You can do 3x larger context window, processing 3x faster than before!They are first that have integrated Google turboquant in local models and made it accessible for everyone for free
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用通俗易懂的语言解释 TurboQuant——谷歌如何将AI memory缩减 6 倍
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