ODCC联合NVIDIA、焱融等首发KVCache评测结果｜焱融AI存储实现推理提速降本双突破

DoNews03-06 16:58

随着大模型在企业场景中的快速落地，AI 推理系统正面临新的基础设施挑战。上下文长度不断增长、用户并发请求数量快速上升，使得传统 GPU 显存架构逐渐成为制约推理效率的关键瓶颈。如何通过系统架构优化释放算力潜力，保证推理性能与控制成本，已成为企业掘金 AI 商机的核心命题。近日，开放数据中心委员会（ODCC）在 NVIDIA、美团、三星、Solidigm 等产业链领军企业支持下成立的 AI 存储...

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reduction","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDY":0.6,"2NVD.UK":0.6,"NVDU":0.6,"NVDX":0.6,"NVDA":0.9,"NVII":0.6,"SNVD.UK":0.6,"NVD3.UK":0.6,"ANV":0.6,"NVDB":0.6,"NVDS.UK":0.6,"NVIW.SI":0.6,"NVD":0.6,"NVDS":0.6,"DIPS":0.6,"3NVD.UK":0.6,"AIYY":0.6,"NVDQ":0.6,"NVDW":0.6,"NVD2.UK":0.6,"NVDD":0.6,"NVDG":0.6},"content_text":"随着大模型在企业场景中的快速落地，AI 推理系统正面临新的基础设施挑战。上下文长度不断增长、用户并发请求数量快速上升，使得传统 GPU 显存架构逐渐成为制约推理效率的关键瓶颈。如何通过系统架构优化释放算力潜力，保证推理性能与控制成本，已成为企业掘金 AI 商机的核心命题。近日，开放数据中心委员会（ODCC）在 NVIDIA、美团、三星、Solidigm 等产业链领军企业支持下成立的 AI 存储实验室首次发布针对大模型推理关键制约因素——KV Cache 的专项评测结果。测试结果显示，焱融科技自主研发的 YRCache 推理存储系统实现了推理性能的数量级跃升与成本的革命性优化，为 AI 规模化落地提供了全新的“存储驱动推理”范本。权威测试数据揭晓：TTFT 与 TPOT 降低97%，吞吐量提升 22 倍作为国内专业的 AI 存储厂商，焱融自研的 YRCache 推理存储系统专为大规模推理设计，通过构建 GPU 显存、主机内存、本地 NVMe SSD 及 YRCloudFile 高性能分布式存储的多级缓存架构，旨在显著扩展 KV 缓存空间，打破显存限制。本次测试在基于 NVIDIA 计算和网络平台的真实推理环境中进行，采用DeepSeek-R1等主流大模型，覆盖了中端 GDDR GPU 与高端 HBM GPU 两类算力节点，对比了在不同网络带宽配置（200Gbps / 400Gbps / 800Gbps）下，原生 vLLM 框架与集成 YRCache 后的系统性能。本次测试核心成果亮点显著：推理性能全面数量级提升在 Batch 值为 16、输入长度 10K Tokens 的典型环境下，集成 YRCache 后，首 Token 延迟（TTFT）和单个 Token 生成时间（TPOT）可优化高达 97%，系统 Token 吞吐量最高提升 22 倍。这意味着用户将体验到“即问即答”的瞬时响应，长文档生成如丝般顺滑。同时，系统吞吐能力大幅增强，能够服务更多并发用户请求，单 token 成本也同比例降低。2.长上下文场景表现稳定且性能提升增益放大在模拟输入 Token 从 100 扩展至 100K 的测试中，YRCache 始终保持稳定性能优势，且随着上下文增长，其加速效果持续放大。这为企业在长文档分析、多轮对话等高负载任务中，提供了避免性能断崖的技术底气。3.中低配 GPU 推理性能接近追平高配卡，推理成本实现结构性优化测试揭示了一项更具战略价值的成果。在YRCache加持下，中端 GDDR GPU 服务器的综合推理性能，已大幅逼近甚至在某些维度媲美高端 HBM GPU 原生方案。具体数据显示，原生状态下中端 GPU 吞吐量仅为高端的30%，而在使用YRCache后，两者差距急剧缩小，可达到高端 HBM GPU 的 79%。投资回报率（ROI）飙升14倍，重构 AI 推理经济模型更直观的商业价值体现在投入产出比。从测试数据推算可看出，虽然原生状态下中端 GDDR GPU 表现不占优，但引入 YRCache 优化后，其 ROI 相比原生高端 HBM GPU 方案，在400Gbps和800Gbps网络环境下分别提升了 11 倍和14 倍。这意味着，在相同资金投入下，采用“中端GDDR GPU服务器 + YRCache”方案，能够带来远超高端HBM GPU原生方案的推理产出效率。这一突破重构了企业AI推理的成本结构——当推理成本的重心从依赖高端GPU转向依托存储技术创新，AI应用的盈亏平衡点将大幅下移。中小企业能以更低门槛部署高性能推理服务，而大规模AI企业则能显著优化TCO（总拥有成本），为更多创新场景打开经济可行性大门。迈向“存储驱动推理”的新范式本次 ODCC 的首发评测，不仅验证了焱融 YRCache 的技术实力，更标志着“以存促算、架构降本”的AI推理新路径的可行性。作为 ODCC AI 存储实验室的重要实践，该测试为行业提供了可量化、可复现的技术参考，推动AI存储技术向标准化、规范化迈进。ODCC AI 存储实验室表示，将持续推进 KV Cache 系列测试，深化“部件—系统—应用”全栈协同。焱融科技也强调，YRCache 同时支持 PD（Prefill-Decode）分离等下一代推理架构，持续以“数量级性能提升+颠覆性成本优化”的双重能力，助力企业在 AI 规模化落地的浪潮中以更低成本、更高效率抢占先机。声明：本站转载此文目的在于传递更多信息，并不代表赞同其观点和对其真实性负责。如涉及作品内容、版权和其它问题，请在30日内与本网联系，我们将在第一时间删除内容,本网站对此声明具有最终解释权。","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":"corporation","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":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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