2022年12月6日

寒武纪- ai研究有限责任公司创始人兼首席分析师得到它!该公司声称，它可以比AWS更快地训练AI 8X，而大型号的成本只有AWS的一半。Cerebras并不是典型的人工智能芯片公司。这是一件好事。我们已经有很多模仿NVIDIA的产品了，而且它们似乎都做得很好。现在Cerebras宣布在Cirrascale上提供新的云接入，并以固定价格计划在其硬件上训练基础模型。ONE Cerebras CS-2系统在涡轮冷却底盘中使用晶圆级“芯片”，成本可达数百万美元。为了训练真正的大型模型，您可能需要四台消耗机架的机器，再加上MemoryX和ScaleX基础设施来养活这个庞然大物。让我们看看你为什么要尝试，以及你可以如何去做。雷竞技 raybet Each Cerebras CS-2 is so large and power-hungry, you can only install one per rack. Cerebras Systems, Inc. AI is undergoing massive changes this year, spearheaded by the interest in large language models, now called Foundation Models since one can build new applications on these generational AI’s. Cerebras believes its massive wafer-scale engines can train these large models much more efficiently that hundreds of GPUs, but they need to lower the bar to give it a try. These models that generate text, images, and video from natural language prompts need a lot of computational power. In fact, if you don’t need a Cerebras to train a large model, then you probably don’t need a Cerebras. Cerebras CEO, Andrew Feldman, recognizes he and his team has invented something very special; a machine that can solve problems a GPU can not solve. A machine so powerful, they needed to invent the fabric (SwarmX) and weight-feeding server to interconnect and feed massive data sets to the wafer-scale engine. And finally, a machine so expensive they had to rethink access so the community can experiment and build the foundation models everyone is clambering for. As a reminder, a single CS-w contains 2.6 Trillion 7nm transistors, 850,000 cores, and consumes 15kW of power. MORE FOR YOU