> Want it to go away, almost like magic? Local inference. When its under your control, and no longer being forced to hold it wrong, all of the common LLM defects will go away.

This is just not true. Any local LLM you can host on consumer-accessible hardware has all of these defects, too. Adjusting the knobs doesn’t solve everything.

The closest you can get to frontier performance is Kimi K3, but you’re not hosting that unless your budget is on the order of a nice house in a good metro area.

I like my local LLMs as much as the next person and my office is currently uncomfortably warm from the amount of compute happening, but I would never agree that local LLMs solve all of the common LLM defects. This is peak wishful thinking.

In my experience, the local models and even the larger ones that we can’t run at home suffer more from long context degradation than the frontier models. You are exactly right that you need to manage context length, but even at fp16/bf16 the local models have a lower ceiling for usable context length in my experience.

Do you work for a frontier lab by any chance?