Quantization is important for me because it's the only way out I can see for a future of programming that doesn't involve going through a giant bigco who can run, as the article says, a machine with 2TB of memory. And not just memory, but my understanding is that for the model to be performant, it has to be VRAM to boot.

This comes as the latest concern of mine in a long line around "how software gets written" remaining free-as-in-freedom. I've always been really uneasy about how reliant many programming languages were on Jetbrains editors, only vaguely comforted by their "open-core" offering, which naturally only existed for languages with strong OSS competition for IDEs (so... java and python, really). "Intellisense" seemed very expensive to implement and was hugely helpful in writing programs without stopping every 4 seconds to look up whether removing whitespace at the end of a line is trim, strip, or something else in this language. I was naturally pleased to see language servers take off, even if it was much to my chagrin that it came from Microsoft, who clearly was out of open standards to EEE and decided to speed up the process by making some new ones.

Now LLMs are the next big worry of mine. It seems pretty bad for free and open software if the "2-person project, funded indirectly by the welfare state of a nordic or eastern-european nation" model that drives ridiculously important core libre/OSS libraries now is even less able to compete with trillion dollar corporations.

Open-weight, quantized, but still __good__ models seem like the only way out. I remain somewhat hopeful just from how far local models have come - they're significantly more usable than they were a year ago, and we've got more tools like LM Studio etc making running them easy. But there's still a good way to go.

I'll be sad if a "programming laptop" ends up going from "literally anything that can run debian" to "yeah you need an RTX 7090, 128GB of VRAM, and the 2kW wearable power supply backpack addon at a minimum".

I've been watching the drizzle of LLM papers come through, and I think we're going to hit a 1T param MoE on consumer hardware before this year is out. It'll still be behind the bigco models, but it'll be a force multiplier. Ideally, we'd get these models to run on a CPU. MS BitNet is one way to do this. You can already run ternary LLMs on consumer CPUs with a decent tps.

You can still continue to master actual software engineering while others spend their time turning their minds into a palimpsest of tricks and lessons of how to convince one model after another after another after another into giving reasonable output. That you'd still have to vet yourself anyway.

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