(1) Opus 4.5-level models that have weights and inference code available, and
(2) Opus 4.5-level models whose resource demands are such that they will run adequately on the machines that the intended sense of “local” refers to.
(1) is probable in the relatively near future: open models trail frontier models, but not so much that that is likely to be far off.
(2) Depends on whether “local” is “in our on prem server room” or “on each worker’s laptop”. Both will probably eventually happen, but the laptop one may be pretty far off.
I was thinking about this the other day. If we did a plot of 'model ability' vs 'computational resources' what kind of relationship would we see? Is the improvement due to algorithmic improvements or just more and more hardware?
i don't think adding more hardware does anything except increase performance scaling. I think most improvement gains are made through specialized training (RL) after the base training is done. I suppose more GPU RAM means a larger model is feasible, so in that case more hardware could mean a better model. I get the feeling all the datacenters being proposed are there to either serve the API or create and train various specialized models from a base general one.
Not really. A 100 loc "harness" that is basically a llm in a loop with just a "bash" tool is way better today than the best agentic harness of last year.
Opus 4.5 is at a point where it is genuinely helpful. I've got what I want and the bubble may burst for all I care. 640K of RAM ought to be enough for anybody.
I don't get all this frontier stuff. Up to today the best model for coding was DeepSeek-V3-0324. The newer models are getting worse and worse trying to cater for an ever larger audience. Already the absolute suckage of emoticons sprinkled all over the code in order to please lm-arena users. Honestly, who spends his time on lm-arena? And yet it spoils it for everybody. It is a disease.
Same goes for all these overly verbose answers. They are clogging my context window now with irrelevant crap. And being used to a model is often more important for productivity than SOTA frontier mega giga tera.
I have yet to see any frontier model that is proficient in anything but js and react. And often I get better results with a local 30B model running on llama.cpp. And the reason for that is that I can edit the answers of the model too. I can simply kick out all the extra crap of the context and keep it focused. Impossible with SOTA and frontier.
GLM 4.7 is already ahead when it comes to troubleshooting a complex but common open source library built on GLib/GObject. Opus tried but ended up thrashing whereas GLM 4.7 is a straight shooter. I wonder if training time model censorship is kneecapping Western models.
Just try calculating how many RTX 5090 GPUs by volume would fit in a rectangular bounding box of a small sedan car, and you will understand how.
Honda Civic (2026) sedan has 184.8” (L) × 70.9” (W) × 55.7” (H) dimensions for an exterior bounding box. Volume of that would be ~12,000 liters.
An RTX 5090 GPU is 304mm × 137mm, with roughly 40mm of thickness for a typical 2-slot reference/FE model. This would make the bounding box of ~1.67 liters.
Do the math, and you will discover that a single Honda Civic would be an equivalent of ~7,180 RTX 5090 GPUs by volume. And that’s a small sedan, which is significantly smaller than an average or a median car on the US roads.
I didn’t do the napkin math on it earlier, because I don’t believe it really matters for making the point I was making.
I don’t care about looking up real numbers, so I will just overestimate heavily. Let’s say that for a large enough number of GPUs, the overhead of all the surrounding equipment would be around 20% (amortized).
So you can just take the number of GPUs I calculated in my previous comment, multiply by 0.8, and you get your answer.
