If I could have shown up somewhere in 2022 with a Mac Studio M1 Max w/ 64GB of RAM running Qwen-3.6-27B or 35B-A3B, I would have pretty much been a demigod - to a degree far more impressive than being able to run Opus 5 locally today.

So yes, I think your scenario is likely to eventually happen, but there will be a much more powerful, capable frontier model then.

There’s no reason to assume frontier-level intelligence eventually collapses all the way onto a midrange consumer GPU. In fact, there are quite a few reasons not to assume that (information-theoretic constraints, etc).

Workloads will inflate just as they have been. Remember when llm assisted development used to be good only for a function, then a whole file, then a handful of files, then a code base, then a full stack, etc etc etc.

People will claim to have “enough” even though they already have the equivalent of last years capabilities locally.

probably not all that much... the market would dip, just like every time a new open weights model gets announced. but hundreds of millions of people aren't going to immediately self-hosting their own models.

the biggest winner in that scenario would be ai providers, who suddenly have a capable model that they can serve much more efficiently. and the incumbents have a whole lot of compute. wouldn't anthropic and openAI just start offering that open weights model at prices that nobody else could compete with?

Those companies will be quick to copy the tech, inference cost would plummet and there is a greater chance that these companies could make it to solvency. At least in the short term. Long term it might not be so great as consume hardware catches up.

Nothing would really change IMO? 99% of users don't have anything like a RTX5070 (mobile especially).

Even if it did, it still doesn't make much economic sense running a model locally vs on a datacentre.

For example, I managed to just about squeeze a Q2 quant of Qwen 3.7 27b on my 9070XT. I get around 60tps decode (slightly faster prefill). _but_ it uses 300W of power to do so. At UK electricity rates of 30c/kWh this works out at something like 42c/MTok. I can get far far better models on openrouter cheaper than that, plus I'm not horrendously constrained on context length.

If you could run Opus 5 on a 5070 then the labs must have achieved RSI at that point

> on an RTX 5070

RTX 5070 prices go up ~N times. Nvidia makes more money because it's easier to make these things than it's to make a GB300.

It seems very very unlikely that an Opus 5 matching local model that runs on a 5070 will be released within the next 5 years (I don't want to say "ever").

If it does happen then NVidia will sell a lot of 5070s though!

inference is the cheap part; training is expensive. what compute infrastructure would train this mythical magic model?

Inference time scaling means whoever had the most compute has the highest intelligence model.

I guess I'd like to understand the technical reasoning on how you think an how an Opus 5 could over time fit on an RTX 5070.