Hugging Face 儲存架構演進：從檔案到分塊（Chunks）提升儲存效率
Original: From Files to Chunks: Improving HF Storage Efficiency
The Hugging Face Hub currently hosts millions of AI models, datasets, and applications (Spaces), with total storage reaching the hundreds…
Hugging Face 發表全新的儲存優化方案，將傳統的檔案級儲存（如 Git LFS）轉型為「分塊儲存（Chunk-based Storage）」。透過內容定義分塊（CDC）與內容定址儲存（CAS）技術，Hub 能跨儲存庫進行資料去重。這對於微調（Fine-tune）與合併（Merge）模型的儲存能節省極大空間，並顯著加快上傳與下載速度。
The Hugging Face Hub currently hosts millions of AI models, datasets, and applications (Spaces), with total storage reaching the hundreds of petabytes. As the community of open-source model fine-tuning, quantization, and model merging (for models like Llama and Mistral) has flourished, the traditional file-based storage approach (such as Git LFS) has faced enormous challenges in terms of space and bandwidth.
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