Unverified50% confidenceFactExact time
以FP32存储一个拥有1亿参数的模型需要约400MB内存
1
Sources
50%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
AI应用工程师面试指南：模型量化与推理部署核心考点
redditr/learnmachinelearning7/9/2026
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VerifiedFP16精度下，大模型每10亿参数约需2GB显存77% similarUnverified100万Token在FP16精度下仅KV Cache一项即可消耗数百GB显存，远超单张A100/H100的80GB容量上限75% similarUnverified对于一个典型的700亿参数模型，KV缓存每个词元大约占半兆（0.5MB）内存，生成10万个词元需要携带50GB的缓存73% similarUnverified量化可将700亿参数模型的存储体积从FP16的约140GB压缩至INT4的约35-40GB，能在单张RTX 4090配合CPU内存卸载的消费级硬件上运行73% similarPartially Verified标准的FP32模型每个参数占用4字节，而INT4量化将其压缩至0.5字节，理论上可实现8倍的内存节省72% similar
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