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).

> 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.

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.

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

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.

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.