使用 🤗 Optimum Intel 與 fastRAG 在 CPU 上實現最佳化嵌入向量生成
Original: CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG
When building Retrieval-Augmented Generation (RAG) systems, converting large volumes of text into embeddings (vectors) is an indispensable…
Hugging Face 與 Intel 合作展示如何使用 Optimum Intel 和 fastRAG 框架優化 CPU 上的嵌入向量（Embeddings）計算。透過 OpenVINO 和 Intel Extension for PyTorch (IPEX) 等技術，開發者無需昂貴的 GPU，即可在標準 Intel CPU 上實現高效能、低延遲的 RAG 檢索系統，顯著降低企業部署成本。
When building Retrieval-Augmented Generation (RAG) systems, converting large volumes of text into embeddings (vectors) is an indispensable and computationally intensive step. While GPUs are typically the go-to option for accelerating this process, running inference on existing CPU infrastructure can offer extremely high cost-efficiency for many enterprises.
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