电子行业AI的MEMORY时刻6：英伟达ICMS平台对SSD使用效果好

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英伟达推出AI 推理上下文存储平台ICMS。随用户多轮会话与Agent 持续运行带来的KVCache 不断累积，系统对可长期留存并按需回填的分层KVCache 形成刚性需求，推动上下文从HBM 外溢至DRAM、SSD 等分层介质承接。为此，NVIDIA 推出上下文记忆存储架构ICMS，面向Agent 与多轮推理场景提供“长期上下文记忆层”，一方面承载更大规模KVCache，另一方面以低延迟将历史...

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单位成本显著低于GPU 内存，且可按TB、PB 容量扩展，是长期上下文的天然承载介质。可行性方面，根据《Context Memory Storage Systems, Disruption ofAgentic AI Tokenomics, and Memory Pooling Flash vs DRAM》，ICMS 引入PB 级存储层后，其访问延迟仅较池化DRAM 略高。实证方面，WEKA 对其增强型内存网格（AMG）进行性能评估，AMG 是一种与NVIDIA 的ICMS 兼容的上下文存储解决方案。测试在 decode 阶段模拟用户池持续扩张：（1）当初期用户池较小，KVCache基本驻留在GPU HBM，三种方案（HBM+WEKA AMG、HBM+DRAM、HBM+DRAM+POSIX 文件系统）的token 吞吐均能维持在高位。（2）随着用户数持续增长，KVCache 会外溢到更低层级的内存/存储，token 吞吐开始下滑；但WEKA AMG 凭借更高容量、以及更强的网络与并发随机访问能力，能够更快完成上下文预取与回填，降低冷启动与阻塞，从而在大用户池阶段保持更高且更稳定的token 吞吐，相较HBM+DRAM 与HBM+DRAM+POSIX 方案，其吞吐提升最高可达4 倍，验证ICMS 可有效承接长期上下文并维持吞吐稳定性。ICMS 平台打开存储市场空间。参考vastdata，我们对上下文存储空间大小进行测算。（1）单个token 所需存储空间：假设有10 万个同时在线用户或Agent 使用Llama 3.1 405B，则单个token 的存储空间为504KB/token；（2）用户上下文窗口所需存储空间：若每个上下文窗口为64000 tokens，则对应存储约为30GB。（3）留存倍数：为获得更好的用户体验，假设留存倍数为15×。在上述假设下，10 万用户的总存储需求约为45PB，即若要在具备强对话历史能力的大上下文模型上稳定支持10 万用户/Agent，所需上下文存储规模可达PB 级。投资建议。AI 的Memory 时刻，AI 记忆成为支撑上下文连续性、个性化与历史信息复用的底层能力，持续扩展模型能力边界，有望促进AI Agent 等应用加速落地。AI 记忆的价值正从“费用项”转变为“资产项”，相关上游基础设施价值量、重要性将不断提升。建议关注产业链核心受益标的。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"productRelease","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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