AI Dictionary › Fondamenti AI
Scalabilità
Scalability is a system's ability to sustain load growth while maintaining acceptable performance, by adding resources proportionately. A scalable system serves ten users or ten million by changing size, not architecture. It is the property separating prototypes from products, and the startups that survive success from those that collapse under it.
Two paths: vertical scalability (scale up) strengthens the single machine, more CPU, more memory, simple but with a physical ceiling and rising costs; horizontal scalability (scale out) adds machines, potentially without limit, at the price of running a distributed system with load balancers, sharding and replication. Cloud autoscaling adds and removes resources automatically as traffic moves.
In AI, scalability dominates every discussion: training scales across clusters of tens of thousands of GPUs following the scaling laws that tie model capability to data and compute, while inference must scale with users, multiplying model replicas behind balancers. Beyond AI it is the design criterion of every web service from social networks to payments.
The term derives from scale, from the Latin scala (staircase), through the English verb to scale, to change size by proportion. In systems engineering the question exploded with the growth of the internet: the web-scale era of the 2000s, when Google, Amazon and social networks had to invent architectures for unprecedented loads, produced most of the horizontal scalability techniques that are standard today.
Grace scales with you: from your first gym session through the 520 professional scenarios to the certification exam, the path grows with your level. In the architecture scenarios you learn to ask the AI for designs that withstand growth, distinguishing scale up from scale out.
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