Local models tend to be more efficient since people will be more likely to run compressed and MoE models.

Also, it's basically 1 GPU >= 1 request for the most part in data centres, each request is its own LLM. Each time you make a new request after a set timeout, model weights get loaded in VRAM, context gets initialized, the query gets parsed, and it spits out tokens. These frontier models can be 300 GB in size or more, which all needs to be kept in VRAM for best performance, usually distributed across multiple GPUs, and each loaded model can only answer one query at a time.

Compare this to someone like me, trying to cram Qwen 3.6 MoE on the 16 GB RX 6800XT I already have in my own computer, not using up drinking water or prime real estate to cool my PC, powered using hydroelectricity.

It's so much more eco-friendly and economical, I wish frontier models would just die tbh, or at least only be used for distillation. LLMs offer diminishing returns past a certain point, and you can get 90% of the frontier model with a MoE local model.