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  新智元报道  

编辑：桃子 好困

【新智元导读】AI世界的进化快的有点跟不上了。刚刚，全球最强最大AI芯片WSE-3发布，4万亿晶体管5nm工艺制程。更厉害的是，WSE-3打造的单个超算可训出24万亿参数模型，相当于GPT-4/Gemini的十倍大。

全球最快、最强的AI芯片面世，让整个行业瞬间惊掉了下巴！

就在刚刚，AI芯片初创公司Cerebras重磅发布了「第三代晶圆级引擎」（WSE-3）。

性能上，WSE-3是上一代WSE-2的两倍，且功耗依旧保持不变。

90万个AI核心，44GB的片上SRAM存储，让WSE-3的峰值性能达到了125 FP16 PetaFLOPS。

这相当于52块英伟达H100 GPU！

不仅如此，相比于800亿个晶体管，芯片面积为814平方毫米的英伟达H100。

采用台积电5nm制程的WSE-3，不仅搭载了40000亿个晶体管（50倍），芯片面积更是高达46225平方毫米（57倍）。

专为AI打造的计算能力

此前，在传统的GPU集群上，研究团队不仅需要科学地分配模型，还必须在过程中处理各种复杂问题，比如处理器单元的内存容量、互联带宽、同步机制等等，同时还要不断调整超参数并进行优化实验。

更令人头疼的是，最终的实现很容易因为小小的变动而受到影响，这样就会进一步延长解决问题所需的总时间。

相比之下，WSE-3的每一个核心都可以独立编程，并且专为神经网络训练和深度学习推理中，所需的基于张量的稀疏线性代数运算，进行了优化。

而团队也可以在WSE-3的加持下，以前所未有的速度和规模训练和运行AI模型，并且不需要任何复杂分布式编程技巧。

单芯片实现集群级性能

其中，WSE-3配备的44GB片上SRAM内存均匀分布在芯片表面，使得每个核心都能在单个时钟周期内以极高的带宽（21 PB/s）访问到快速内存——是当今地表最强GPU英伟达H100的7000倍。

超高带宽，极低延迟

而WSE-3的片上互连技术，更是实现了核心间惊人的214 Pb/s互连带宽，是H100系统的3715倍。

单个CS-3可训24万亿参数，大GPT-4十倍

由WSE-3组成的CS-3超算，可训练比GPT-4和Gemini大10倍的下一代前沿大模型。

再次打破了「摩尔定律」！2019年Cerebras首次推出CS-1，便打破了这一长达50年的行业法则。

官方博客中的一句话，简直刷新世界观：

在CS-3上训练一个万亿参数模型，就像在GPU上训练一个10亿参数模型一样简单！

显然，Cerebras的CS-3强势出击，就是为了加速最新的大模型训练。

它配备了高达1.2PB的巨大存储系统，单个系统即可训出24万亿参数的模型——为比GPT-4和Gemini大十倍的模型铺平道路。

简之，无需分区或重构，大大简化训练工作流提高开发效率。

在Llama 2、Falcon 40B、MPT-30B以及多模态模型的真实测试中，CS-3每秒输出的token是上一代的2倍。

而且，CS-3在不增加功耗/成本的情况下，将性能提高了一倍。

除此之外，为了跟上不断升级的计算和内存需求，Cerebras提高了集群的可扩展性。

上一代CS-2支持多达192个系统的集群，而CS-3可配置高达2048个系统集群，性能飙升10倍。

具体来说，由2048个CS-3组成的集群，可以提供256 exafloop的AI计算。

能够在24小时内，从头训练一个Llama 70B的模型。

相比之下，Llama2 70B可是用了大约一个月的时间，在Meta的GPU集群上完成的训练。

与GPU系统的另一个不同是，Cerebras晶圆规模集群可分离计算和内存组件，让开发者能轻松扩展MemoryX单元中的内存容量。

得益于Cerebras独特的Weight Streaming架构，整个集群看起来与单个芯片无异。

换言之，一名ML工程师可以在一台系统上开发和调试数万亿个参数模型，这在GPU领域是闻所未闻的。

具体来说，CS-3除了为企业提供24TB和36TB这两个版本外，还有面向超算的120TB和1200TB内存版本。（之前的CS-2集群只有1.5TB和12TB可选）

单个CS-3可与单个1200 TB内存单元配对使用，这意味着单个CS-3机架可以存储模型参数，比10000个节点的GPU集群多得多。

除此之外，与使用GPU相比，在Cerebras平台上开发所需的代码量还减少了高达97%。

更令人震惊的数字是——训练一个GPT-3规模的模型，仅需565行代码！

Playground AI创始人称，GPT-3正稳步成为AI领域的新「Hello World」。在Cerebras上，一个标准的GPT-3规模的模型，只需565行代码即可实现，创下行业新纪录。

首个世界最强芯片打造的超算来了

由G42和Cerebras联手打造的超级计算机——Condor Galaxy，是目前在云端构建AI模型最简单、最快速的解决方案。

它具备超过16 ExaFLOPs的AI计算能力，能够在几小时之内完成对最复杂模型的训练，这一过程在传统系统中可能需要数天。

其MemoryX系统拥有TB级别的内存容量，能够轻松处理超过1000亿参数的大模型，大大简化了大规模训练的复杂度。

与现有的基于GPU的集群系统不同，Condor Galaxy在处理GPT这类大型语言模型，包括GPT的不同变体、Falcon和Llama时，展现出了几乎完美的扩展能力。

这意味着，随着更多的CS-3设备投入使用，模型训练的时间将按照几乎完美的比例缩短。

而且，配置一个生成式AI模型只需几分钟，不再是数月，这一切只需一人便可轻松完成。

在简化大规模AI计算方面，传统系统因为需要在多个节点之间同步大量处理器而遇到了难题。

而Cerebras的全片级计算系统（WSC）则轻松跨越这一障碍——它通过无缝整合各个组件，实现了大规模并行计算，并提供了简洁的数据并行编程界面。

此前，这两家公司已经联手打造了世界上最大的两台AI超级计算机：Condor Galaxy 1和Condor Galaxy 2，综合性能达到8exaFLOPs。

G42集团的首席技术官Kiril Evtimov表示：「我们正在建设的下一代AI超级计算机Condor Galaxy 3，具有8exaFLOPs的性能，很快将使我们的AI计算总产能达到16exaFLOPs。」

如今，我们即将迎来新一波的创新浪潮，而全球AI革命的脚步，也再一次被加快了。

参考资料：

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