Unsloth AI發佈1.8bit量化Kimi K2模型，顯著降低部署成本
Unsloth AI成功將Moonshot AI的Kimi K2模型量化爲1.8bit版本，模型體積從1.1TB壓縮至245GB，降幅達80%，同時保持全部性能。這款1萬億參數的開源大模型採用MoE架構，擅長代碼生成和推理任務。量化後的版本可在512GB內存的M3Ultra設備上運行，大幅降低部署成本。該技術突破使Kimi K2成爲GPT-4.1等商業模型的有力競爭者，爲中小企業提供高性能AI解決方案。儘管商業化需遵守開源標註要求，但這一進展顯著推動了開源AI生態發展，有望在教育、醫療等領域實現更廣泛應用。
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