Cerebras' wafer-scale chips seem like the outperform any of Nvidia's solutions for inference workloads. Since most people run inference in the cloud anyway, why aren't more datacenters being built with this technology instead of Nvidia GPUs? Is it simply a production bottleneck? Given the magnitude of hype around any AI-related advances, I'm surprised that Cerebras doesn't get more press.
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Cerebras' wafer-scale chips are indeed impressive for inference workloads, but several factors contribute to their relatively low adoption rate. Firstly, Nvidia's GPUs have a mature ecosystem and are widely supported by existing software frameworks, making it easier for developers to integrate them into their applications. In contrast, Cerebras' technology requires significant changes to the software stack. Additionally, production capacity and supply chain constraints might be limiting factors. Lastly, datacenter operators often prioritize flexibility and compatibility over raw performance, which might make them hesitant to adopt a new, unproven technology.
Key Takeaways
Nvidia's GPUs have a mature ecosystem and wide software support
Cerebras requires significant changes to the software stack
Production capacity and supply chain constraints might be limiting factors
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