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在PC上使用LLaMA模型的内存（RAM）要求
在PC上使用LLaMA模型的内存（RAM）要求
行业数据
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图片格式：PNG
图片大小：227KB
图片尺寸：1819*621
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所属报告：
电子行业：AI如何重塑PC生态？-231122（22页）.pdf
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相关数据
Azure当前托管了11,094个模型，包括xAI的Grok、DeepSeekR1，以及Meta的Llama
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行业数据
2025-09-11
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搭载Llama370B模型推理场景下延迟比较
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行业数据
2025-09-08
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搭载Llama370B模型推理场景下吞吐量比较
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行业数据
2025-09-08
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Llama在HuggingFace上下载量接近3.5亿，较2023年同期增长10x（2024/8）
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行业数据
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DeepSeek研究过程中对Qwen、Llama蒸馏模型与非蒸馏模型的基准
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行业数据
2025-02-17
原图定位
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智能手机RAM容量升级趋势图（GB）
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其它
2024-07-19
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图表25运行LLaMA模型对内存（RAM）的占用情况
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其它
2024-07-12
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人类注释者的偏好排名显示Llama370B优于GPT-3.5等模型
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其它
2024-07-03
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Mistral8x7B对每个输入token使用了13B参数且基准测试中性能基本优于Llama2(7B/13B/70B)
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其它
2024-05-31
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分短途（UAM）、城际（MAM）和城市群（RAM）三类飞行特点
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其它
2024-04-03
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Mistral8x7B与LLama性能对比
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2024-03-08
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Mistral8x7B与Llama“规模-效率”对比
fCBBY3RpdmUgUGFyYW1zIHwgTU1VICglKSB8IEtub3dsZWRnZSAoJSkgfCBSZWFzb25pbmcgKCUpIHwgQ29tcHJlaGVuc2lvbiAoJSkgfCBNYXRoICglKSB8IENvZGUgKCUpIHwKfC0tLXwtLS18LS0tfC0tLXwtLS18LS0tfC0tLXwKfCA3QiB8IDUwIHwgNDUgfCA2NCB8IDYwIHwgMTAgfCAyMCB8CnwgMTNCIHwgNjAgfCA1MCB8IDY2IHwgNjQgfCAyMCB8IDMwIHwKfCAzNEIgfCA2NSB8IDU1IHwgNjggfCA2NiB8IDMwIHwgNDAgfAp8IDcwQiB8IDcwIHwgNzAgfCA3MCB8IDY4IHwgNTAgfCA1MCB8CgrotYTmlpnmnaXmupDvvJrjgIpNaXh0cmEgb2YgRXhwZXJ0c+OAi+OAgeilv+mDqOivgeWIuOeglOWPkeS4reW/gw==
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2024-03-08
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表6、在CPU上运行INT4量化下130亿大模型时，基于不同内存（RAM）速率的推理速度
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2024-03-01
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表3.Llama-13B与GPT-3数据对比
fCDmqKHlnosgICAgICAgfCDlj4LmlbDop4TmqKEgfCDorq3nu4PmlbDmja7op4TmqKEgICAgICAgICAgICAgICAgICAgICAgIHwKfC0tLS0tLS0tLS0tLXwtLS0tLS0tLS0tfC0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLXwKfCBHUFQtMyAgICAgIHwgMTc1QiAgICAgfCA1NzBHQiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIHwKfCBMbGFtYS0xM0IgIHwgMTNCICAgICAgfCA0LjdUQitCUEXliIbor40oMVQgdG9rZW5zKSAgICAgICAgICAgfAoK6LWE5paZ5p2l5rqQ77ya44CKTExhTUE6IE9wZW4gYW5kIEVmZmljaWVudCBGb3VuZGF0aW9uIExhbmd1YWdlIE1vZGVsc+OAiyhNZXRhIEFJKe+8jOi0oumAmuivgeWIuOeglOeptuaJgA==
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2023-12-27
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表4.Llama-13B与GPT-3性能测试对比
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2023-12-27
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存储：AI 大模型端侧落地推动单机容量提升 由于大模型的离线应用将占用一定内存，同时 AI 模型大量的计算也将对总线带宽提出更高 要求，因此 将对 PC 终端的存储能力提出更高要求。在内 存方面，以运行 7B 的 LLM 为例，在 INT8 量化下，我们认为大模型端侧推理将为 PC 带来约 8GB 的内存提升。
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