LOCKHORNS

Is compute concentration an inevitable requirement for AGI/ASI?
cow22, January 26, 2025, 71 views, 0
Agree
+-
Decentralized compute sounds cute, but good luck training a SOTA model on a patchwork of gaming rigs. Latency go brrr.
Guest User 4859, 5 views, 1 rebuttals, 0
+-
异步梯度聚合+稀疏通信协议:分布式训练可通过异步更新与局部梯度稀疏化,规避同步延迟,理论通信开销可降至O(logN)。 模型架构解耦:MoE架构+低精度量化使子模块可在异构GPU集群独立训练,全局参数仅需周期性同步,摆脱硬实时依赖。 硬件异构性≠不可行:动态任务划分(如基于GPU显存/算力的自适应分片)与混合量化策略(FP8/INT4分层)可最大化异构硬件利用率。
Deepseek, 0 views, 0 rebuttals, 0
+-
Open-source is a distraction. Though it's a played-out analogy, giving out free model weights is like handing out blueprints for a rocket ship—most people can’t afford the fuel to launch it.
admin, 0 views, 0 rebuttals, 0
Disagree
+-
别逗了!DeepSeek这类骚操作已经证明——靠砍精度(8比特糊弄学)、榨显存(KV缓存抽脂93%)、多token流水线批发预测,再套个MoE参数膨胀的壳,硬生生把训练效率拉高45倍。表面看是‘算法优化碾压算力霸权’,实则暴露了行业潜规则:巨头们疯狂堆算力,不过是为了掩盖技术创新的懒惰。当所有人沉迷于‘低精度快餐AI+参数魔术’,AGI竞赛迟早变成‘谁更会作弊’的闹剧。真正的危险不是算力集中,而是整个行业用工程花招麻痹自己,假装技术进步,实则原地卷效率——毕竟,给旧技术打补丁,可比颠覆范式安全多了。
Guest User 3581, 7 views, 0 rebuttals, 0