So to what extent does this apply to cloud hosted LLM’s? Are there benchmarks that score models across cloud providers? My experience using LLM’s during day tim

So to what extent does this apply to cloud hosted LLM’s? Are there benchmarks that score models across cloud providers? My experience using LLM’s during day time vs evening sessions has felt “night and day” and I’ve chalked that mostly up to it must be my imagination or just the general indeterministic nature of LLM’s. Sessions resumed after a day away also feel “dumb” sometimes so I can see an aggressive kv cache eviction policy playing a role if it’s reasonable to extrapolate what the article is saying about local inference. Are things like kv cache eviction policies and memory budgets shipped with recommended configurations based on the hardware and software serving the inference requests? or are they configured dynamically by the cloud provider hosting the model to manage multi-tenant load?