從零開始在 nanoVLM 中實作 KV Cache
Original: KV Cache from scratch in nanoVLM
In the inference process of large language models (LLMs) and vision-language models (VLMs), autoregressive decoding is a major performance…
本教學深入探討大語言模型推理加速的核心技術——KV Cache。文章以輕量級視覺語言模型 nanoVLM 為基礎，從原理出發，逐步引導讀者用 PyTorch 從頭實作 KV Cache。內容涵蓋 Prefill 與 Decode 階段的快取處理，並特別解析了多模態情境下視覺 Token 的快取優化，是理解 Transformer 推理底層邏輯的極佳教材。
In the inference process of large language models (LLMs) and vision-language models (VLMs), autoregressive decoding is a major performance bottleneck. Each time the next token is generated, the model must compute attention against all previous tokens. Without optimization, the Key and Value vectors for historical tokens must be recomputed from scratch every time, causing computational complexity to grow quadratically (O(N²)) with sequence length. KV Cache (Key-Value Cache) technology was developed precisely to address this problem: by storing historical K and V vectors in memory, the computational complexity of decoding each new token is reduced to O(N).
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