123 points by samwho 5 hours ago | 10 comments
steve_adams_86 50 minutes ago
Sam's previous posts are well worth digging up too. This one is outstanding, but they're all good. I really enjoyed this and learned a lot.

I'm a bit envious of his job. Learning to teach others, and building out such cool interactive, visual documents to do it? He makes it look easier than it is, of course. A lot of effort and imagination went into this, and I'm sure it wasn't a walk in the park. Still, it seems so gratifying.

aarondf 4 hours ago
My word... samwho is doing some of the best technical explainers on the internet right now.
polotics 2 hours ago
Leading to my question: Ok keeping a zero and a minus-zero does make sense for some limits calculations... But when all you have is 4 bits, is this not quite wasteful? Would using the bits for eg. a 2.5 not improve the model?
polotics 2 hours ago
Oh well that's a rabbit hole: NVIDIA Blackwell has this, also GGUFs sidestep this with Qi_j / Qi_K... Great article, spikes curiosity!
seabass 2 hours ago
Heartily second that! It was cool to see a combination of DOM, SVG, and canvas visualization all in use for this post.
muskstinks 36 minutes ago
The 2 bit is probably slower because it clashes with some register sizes and how data is read in blocks. No additional benefit because the architecture doesn't read 2 bits but probably min 4 bits and then it clashes with utilization.

Really good visualizations overall.

gavinray 1 hour ago
I read the entire thing top-to-bottom, as a visual learner this is superb.

One nitpick -- in the "asymmetric quantification" code, shouldn't "zero" be called "midpoint" or similar? Or is "zero" an accepted mathematics term in this domain?

samwho 1 hour ago
“Zero point” is how I saw it referred to in the literature, so that’s what I went with. I personally prefer to think of it as an offset, but I try to stick with terms folks are likely to see in the wild.
gavinray 1 hour ago
Fair enough, thanks!
samwho 1 hour ago
You’re welcome! Thanks so much for the kind words.
mrsilencedogood 3 hours ago
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".

all2 1 hour ago
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.
add-sub-mul-div 54 minutes ago
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.
mrsilencedogood 19 minutes ago
While I think a lot of the AI hype is just hype - everyone saying most of these things have _hitherto untold riches_ levels of financial incentives to say them - I think it's also undeniable that LLMs speed up many aspects of coding.

I also think that AI might be the beginning of the end of copyright. While before, everyone with money clearly had tremendous incentive to keep copyright strong, now all of a sudden trillions of dollars are basically predicated on the idea that LLMs aren't violating copyright. Copyleft has been a major tool in the FOSS toolbox. If that's weakening, I don't ALSO want free software to be locked out of agentic programming too.

qcautomation 1 hour ago
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armcat 4 hours ago
This is beautifully written and visualised, well done! The KL divergence comparisons between original and different quantisation levels is on-point. I'm not sure people realize how powerful quantisation methods are and what they've done for democratising local AI. And there are some great players out there like Unsloth and Pruna.
samwho 4 hours ago
Thank you! I was really surprised how robust models are to losing information. It seems wrong that they can be compressed so much and still function at all, never mind function quite closely to the original size.

Think we're only going to keep seeing more progress in this area on the research side, too.

buildbot 3 hours ago
You can even train in 4 & 8 bits with newer microscaled formats! From https://arxiv.org/pdf/2310.10537 to gpt-oss being trained (partially) natively in MXFP4 - https://huggingface.co/blog/RakshitAralimatti/learn-ai-with-...

To Nemotron 3 Super, which had 25T of nvfp4 native pretraining! https://docs.nvidia.com/nemotron/0.1.0/nemotron/super3/pretr...

cphoover 4 hours ago
5-10% accuracy is like the difference between a usable model, and unusable model.
samwho 4 hours ago
Definitely could be, but in the time I spent talking to the 4-bit models in comparison to the 16-bit original it seemed surprisingly capable still. I do recommend benchmarking quantized models at the specific tasks you care about.
amelius 1 hour ago
Yes I was wondering why they mentioned those numbers without mentioning their practical significance.
AIorNot 2 hours ago
Man what a brilliant technical essay.. hat's off to the writer for clarity and visualizations.
samwho 1 hour ago
Thank you!
fcpk 3 hours ago
something I have been wondering about is doing regressive layer specific quantization based on large test sets. ie reduce very specifically layers that don't improve general quality.
buildbot 3 hours ago
This is a thing! For example, https://arxiv.org/abs/2511.06516
fcpk 3 hours ago
that's brilliant, I wonder why we haven't seen much use of it to do very heavy quantization
vicchenai 4 hours ago
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