Because this is Microsoft, experimenting and failing is not encouraged, taking less risky bets and getting promoted is. Also no customer asked them to have 1-bit model, hence PM didn't prioritize it.

But it doesn't mean, idea is worthless.

You could have said same about Transformers, Google released it, but didn't move forward, turns out it was a great idea.

The most benign answer would be that they don’t want to further support an emerging competitor to OpenAI, which they have significant business ties to. I think the more likely answer which you hinted at is that the utility of the model falls apart as scale increases. They see the approach as a dead end so they are throwing the scraps out to the stray dogs.

So is it finally time for a Beowulf cluster to do something amazing?

Cannot agree more!

These are trits, which provide their own efficiencies.

Interestingly, a trit x float multiplier is cheaper than a trit x integer multiplier in hardware if you're willing to ignore things like NaNs.

0 and one are trivial, just a mux for identity and zero. But because floats are sign-magnitude, multiply by negative one is just an inverter for the sign bit, where as for integers you need a bitwise inverter and full incrermenter.

You only need GPUs if you assume the training is gradient descent. GAs or anything else that can handle nonlinearities would be fine, and possibly fast enough to be interesting.

You drop the memory throughput requirements because of the packed representation of bits so an FMA can become the bottleneck, and you bypass the problem of needing to upscale the bits to whatever FP the FMA instruction needs.

typically for 1-bit matmul, you can get away with xors and pop_counts which should have a better throughput profile than FMA when taking into account the SIMD nature of the inputs/outputs.

The win is in how many weights you process per instruction and how much data you load.

So it's not that individual ops are faster — it's that the packed representation lets each instruction do more useful work, and you're moving far less data from memory to do it.

Bitnet encoding more information dense per byte perhaps? CPUs have slow buses so would eke out more use of bandwidth?

Approximately as challenging as training a regular 100B model from scratch. Maybe a bit more challenging because there's less experience with it

The key insight of the BitNet paper was that using their custom BitLinear layer instead of normal Linear layers (as well as some more training and architecture changes) lead to much, much better results than quantizing an existing model down to 1.58 bits. So you end up making a full training run in bf16 precision using the specially adapted model architecture

What's unusual about it? It seems pretty standard to train small models to validate an approach, and then show that training scales with model size to 8B to 14B parameter models, which is what they did.

That issue appears to be the one that's wrong. From the technical report

> We evaluated bitnet.cpp in terms of both inference speed and energy cost. Comprehensive tests were conducted on models with various parameter sizes, ranging from 125M to 100B. specific configurations for each model are detailed in the Appendix A.

I browsed through the history of the user and confirm this statement. I know that there are users who say they used em-dashes even before the rise of ChatGPT and HN statistics support that. For example, one prominent example is dang.

However this user uses — in almost all his posts and he had a speed of 1 comment per minute or so on multiple different topics.

Hmm, the user joined in 2019 but had no submissions or comments until just 40 minutes ago (at least judging by the lack of a second page?) and all the comments are on AI related submissions. Benefit of doubt is it'd have to be a very dedicated lurker or dormant account they remembered they had.

Edit: oh, just recalled dang restricted Show HNs the other day to only non-new users (possibly with some other thresholds). I wonder if word got out and some are filling accounts with activity.

Agreed. This is becoming an issue, see also: https://news.ycombinator.com/item?id=47259308

Funny enough I now involuntarily take RTFA as a slight slop signal, because all these accounts dutifully read the article before commenting, unlike most HNers who often respond to headlines.

I would love to understand the thought process behind this. I'm sure it's a fun experiment, to see if it's possible and so on... but what tangible benefit could there be to burning tokens to spam comments on every post?