[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"aihot-art-90723":3},{"itemId":4,"vertical":5,"category":6,"source":7,"score":8,"title":9,"summary":10,"analysis":11,"url":12,"coverUrl":13,"direction":13,"marketSignal":13,"publishedAt":14},"90723","ai","论文研究","SemiAnalysis",59,"Kimi K3 2.8T模型需GB300等大显存GPU部署","Kimi K3因2.8万亿参数规模过大,无法单机部署在B200上,需GB300 NVL72等大显存GPU系统运行。","半导体分析机构SemiAnalysis指出,月之暗面的Kimi K3虽然开源在即,但2.8万亿参数的体量意味着连英伟达最新的DGX B200都无法单机跑起来,揭示了「参数越大越强」背后被忽视的部署门槛。\n\n·即便压到FP4精度,Kimi K3也无法装进单台NVIDIA DGX B200,必须依赖GB300 NVL72、B300或MI355X等更高端系统\n·核心瓶颈是显存,这类系统单卡配备288GB显存,远超主流部署常见规格\n·一种折中方案是用WideEP技术把多个B200节点拼起来,但受限于节点间带宽仅400Gbit\u002Fs\n·相比之下NVL72的节点间带宽是B200组合方案的18倍,差距直接决定了推理效率的天花板\n·这说明「模型开源」和「能低成本部署」是两回事,普通团队拿到权重后未必用得起\n\n对关注Kimi K3的团队来说,真正的门槛不在能否下载权重,而在能否负担GB300级别的硬件投入,这会让「开源即可复现」的预期打个折扣。","https:\u002F\u002Fx.com\u002FSemiAnalysis_\u002Fstatus\u002F2077966560447074689",null,"2026-07-17 12:00:09"]