AI Dictionary › AI Fundamentals
A cluster is a group of connected computers working together as if they were a single system. Each machine is a node; a network joins them and coordination software distributes the work. It is the practical answer to a physical limit: when one machine, however powerful, is no longer enough, you put many together.
Nodes communicate over high-speed networks and share the load according to the cluster type: in compute clusters each node processes a slice of the problem, in high-availability clusters nodes watch each other ready to take over, in storage clusters data is replicated across machines. A scheduler assigns tasks and manages overall resources.
In AI, clusters are the model factory: training large LLMs happens on clusters of thousands of interconnected GPUs, where the model is split and parallelized across nodes. Large-scale inference and distributed vector databases run on clusters too. Beyond AI, clusters power scientific supercomputers, search engines and banking systems.
The English word cluster, a bunch or group, comes from Old English clyster. Multi-machine systems appeared as early as the 1960s and 70s, but a symbolic milestone is 1994, when Thomas Sterling and Donald Becker at NASA built Beowulf, a cluster of ordinary PCs wired together that proved high-performance computing could be achieved with cheap hardware: an idea that paved the way for modern data centers.
Understanding what a cluster is helps you in Grace's professional scenarios that deal with AI infrastructure, training costs and scalability. The 7 evaluation dimensions reward prompts that show command of the technical context the AI operates in.
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