Non-forking httpd servers using select() were a popular little coding challenge for a while in the 90s. Spinner was one of them.
Nginx’s real strength was being able to proxy and cache HTTP using that same mechanism, so you didn’t additionally need to deploy Varnish or some other appliance.
As to whether this is a good mental model for what is coming for local LLMs, I am not sure I am convinced. Apart from more quantisation-aware training, perhaps binary and ternary aware training, custom inference engines per model, and maybe some improvements in diffusion models, the grand challenge in small footprint LLMs is training really small reasoning and tool use models, and so far it’s far from clear they can deliver.
Truly tiny models will not be viable as general coding assistants; even 12B dense is too small and you will find plenty of people who will tell you that 26B/4B or 35B/3B MoE is too. Though perhaps they can be trained for single languages, like just Python or just TS/JS.
More likely is the idea that 30-40B dense models might be good enough for most things once low cost and likely bespoke hardware catches up.
But I don’t think any truly profound advances seem likely in software or training alone. I am no expert but it feels like we’re already a lot closer to efficiency than we were in your analogy, and the gains are perhaps not going to be much more than small increments.
Maybe we will see something like a ternary 60B/10B MoE model turn up. But at the moment at least I am not sure where the incentives are to train these.
Fun fact: Igor Sysoev originally wrote mod_accel [1], an acceleration and reverse-proxy caching module for Apache before he made nginx.
I remember using that module in the mid-2000s as a load balancer (and to offload SSL encryption/decryption as it was a CPU-expensive operation).
My naïve impression is that the LLM world will keep delivering these fractional improvements for some years at the cost of simplicity. And sure, ontological support seems quite promising.
But making things radically better or faster for small models in the way that is hypothesised, that feels like it can only come as a result of hardware performance improvements and likely architecture changes.
Because there's no free lunch, right? Speculative drafting for example, noticeably improves performance until acceptance rates drop for reasons that have to do with the particular application, and then it starts hurting you, especially near the limits of the memory bandwidth. Because once it is wasteful it is an extra overhead.
I gather Gemma 4 supports, in principle, dynamic speculative draft lengths, to help with this — where it will stop making bold predictions when the success rate falls. But I'm not sure if any of the inference engines I've tested with support that.
I think small models are miraculous — I still think Gemma 4 12B is astonishing — but I guess what I am saying is that I think maybe technology is moving quickly enough that the developers are done with the low-hanging fruit.
Even if the idea of "no free lunch" was taken liberally discounting the cost of research, it would only be limiting to systems built from a foundation of optimization, but that's not the case. The foundation so far has been one of brute force scaling. Usually meaning there is lots of room for optimization.
In cloud AI, sure. But in the smaller open weights model territory it feels like we're well into optimisation?
Small llms are still way more efficiently server on big GPUs.
Sharing server capacity takes advantage of the massive parallel throughput and sharing of memory bandwidth.
You are sharing the GPUs with thousands of concurrent users.
Many analysts (and Microsoft) think even now that if everything committed gets built there will be considerable oversupply and there is not the revenue to pay for it.
If small models do continue to improve in unusual ways (I think there are limits) then the marginal need for cloud AI compute could fall precipitously beyond current estimates. The marginal need for consumer AI could almost totally collapse if someone makes good progress on very small reasoning and tool-calling models (which is a modestly big if)
The possibility of the data centre boom resembling the Chinese real estate bubble is not inconsiderable.
Every time they've made smarter models we've wanted the smarter ones, and local models runnable on typical hardware are still very far behind in speed and intelligence (as neat as they are)
That is seemingly not the case. The buildout is actually slow; almost nothing of these giant projects has been completed. Nobody will say how much of anything they have actually finished. And Nvidia have made huge, huge buy-and-hold deals for GPUs that do not have data centres to go into.
Everyone is GPU poor because stuff hasn't been finished but large numbers of GPUs are spoken for, but they are GPU poor on therefore much less demand than is being built for.
Look at how tiny SpaceX's deal is with Anthropic, for example. This meaningfully turned around Anthropic's prospects — allowing them to radically lift rate limits beyond what many users needed -- but it was for just 300 megawatts. Tiny compared to the 31 gigawatts allegedly under construction by the end of last year.
So the picture is partly illusory. GPU prices and RAM prices have been pushed up by the AI firms booking them for data centres they haven't even started building yet, as well as the ones that they've only completed a tenth or an eighth of.
There will be significant oversupply. And if open weights models keep getting good and staying fuel-efficient, that picture gets worse.
I also doubt we can estimate the level of demand based on a single deal between Anthropic and SpaceX (despite which, note, Claude still stuggles at times.) Consider other signals, like Google, who we thought had an insurmountable infra advantage, also renting compute capacity from SpaceX and limiting Meta's usage (along with other clients apparently) to conserve capacity: https://www.cnbc.com/2026/06/28/google-limits-metas-use-of-i...
I am not sure Microsoft thinks there will be an oversupply either; last earnings they announced bumping up their CapEx spend, along with all the other hyperscalers.
Here's a way to estimate how much room there is for demand to grow. Various sources (linked in this comment, along with more analysis: https://news.ycombinator.com/item?id=49089296) indicate that even though a large number of people (50 - 60%) are now using AI at work, they use it for only 6% of their work hours.
That means, even if AI can only address 30% of all work, there is still 5x potential demand growth left! Note, the sources above indicate that AI is even being used in non-knowledge work industries, so the scope is already larger than we thought. This is in addition to the remaining 40 - 50% of people are still not using AI at work. Plus we know that agentic workloads consume way more tokens, so that's yet another multiplier.
But will that demand keep growing? Well, some of those same sources above mention that most executives are planning on ramping up their AI spend in coming years.
Putting all this together explains the hyperscalers' quarterly bemoaning of how strapped for compute they are and why they are spending so much to add more capacity. Given this, an oversupply seems pretty unlikely.
It's a distinction without a difference if your issue is getting hold of a GPU.
But there's a significance to it if you are trying to use demand for a GPU as a proxy for demand for AI. That is where the industry is making serious mistakes.
> I am not sure Microsoft thinks there will be an oversupply either
https://finance.yahoo.com/news/microsoft-ceo-says-overbuild-...
The rest of your comment I am not going to address because it's kind of unfalsifiable. Hypotheses about what AI might be able to do in principle aren't all that useful when talking about even medium-to-long-term demand for what LLMs and GANs can provably do now.
Many tasks suited for AI assistants are background asynchronous tasks. They can run in the downtime where immediate demand is low, keeping overall utilization high enough.
Your argument is similar to those who argue that owning a GPU for gaming doesn't make sense when you can stream from something like GeForce Now. However like with gaming locally (improved latency) there are also benefits to local AI (data privacy).
We will appreciate it even more when popular agents become more costly or start including more ads.
for clarity, I'm not agreeing with GP that small models will mean doom for data center projects
Yes, but the privacy aspect means that for many, many applications slower local will still be preferable to faster remote so long as the actual model performance is the same.
I don't, but holy moly. That sounds insane!
Isn’t it more likely that they will still all be used to the max? I don’t see how at any rate we will be going “alright, that’s enough intelligence for now”
Agree. I know enough about the vagaries of scientific progress to not put any money on any timeline but directionally, that's where we are headed.
> And I think the data center buildout will end in carnage.
Disagree. And this is quite the leap from the previous statement, btw. The carnage happens if the demand for general purpose GPU compute disappears and even then there are so many ways to salvage the asset.
Never thought of it in those terms before.
On the other side, advancement in quantum computers would make current LLM inference much faster. Because of the extreme cooling needed, i dont think the energy demand would become less.
With AI companies talking about AGI, i sometimes wonder if they really need the machines for serving inference to customers, or they have a formula for computational capacity that could run an AGI, and they just want to reach that level.
Last I heard researches simulated an entire brain of a fruitfly with just a classic neuron approach [0] and if I recall it worked great.
I know a human brain is many orders of magnitude more complex, and that there are some birds that use quantum navigation - so it's certainly not impossible by any stretch, though I am quite interested in this statement since it's the first I hear about it.
Doesn't that apply to everything that exists in the material world?