In the human brain most synapses are indeed excitatory, while a minority is inhibitory.

No concise HN comment will give you a complete picture of whats currently known about the human brain, so a platitude necessarily follows:

We call the nearly touching interfaces between neurons synapses, small packets / droplets of neurotransmitter are sent across this interface from the source to the target neuron. Such signals can be excitatory (promote the probability of excitation of the target firing soon) or inhibitory (inhibits the probability of the target firing soon). There are 2 types of sensitive areas on your average neuron: the dendrites (long branching tentacles, that receive excitatory signals) and the cell body where all the signals are accumulated to a local instantaneous "sum" is also sensitive to synaptic activation, but the synapses on the cell body are inhibitory, when sufficiently inhibited the neuron will refuse to fire its axons, so the inhibitory synapses on the cell body can gate the cumulative signal and prevent it from triggering this neuron temporarily. If the neuron does fire, this propagates along the axons (another type of branching tentacles, which lead to yet other neurons, sometimes touching them excitatorily at their dendrite, sometimes touching a neuron inhibitorily at their cell body.

I hope that helped?

I asked this question a while back (the "only train w/ wikipedia LLM") and got pointed to the general-purpose "compression benchmarks" page: `https://www.mattmahoney.net/dc/text.html`

While I understand some of the fundamental thoughts behind that comparison, it's slightly wonky... I'm not asking "compress wikipedia really well", but instead "can a 'model' reason its way through wikipedia" (and what does that reasoning look like?).

Theoretically with wikipedia-multi-lang you should be able to reasonably nail machine-translation, but if everyone is starting with "only wikipedia" then how well can they keep up with the wild-web-trained models on similar bar chart per task performance?

If your particular training technique (using only wikipedia) can go from 60% of SOTA to 80% of SOTA on "Explain why 6-degrees of Kevin Bacon is relevant for tensor operations" (which is interesting to plug into Google's AI => Dive Deeper...), then that's a clue that it's not just throwing piles of data at the problem, but instead getting closer to extracting the deeper meaning (and/or reasoning!) that the data enables.

Correct! I know RAG is a thing, but I wish we could have "DLCs" for LLMs like image generation has LoRa's which are cheaper to train for than retraining the entire model, and provide more output like what you want. I would love to pop in the CS "LoRa or DLC" and ask it about functional programming in Elixir, or whatever.

Maybe not crawl the web, but hit a service with pre-hosted, precurated content it can digest (and cache) that doesn't necessarily change often enough. You aren't using it for the latest news necessarily, but programming is mostly static knowledge a a good example.

When GPT 3.5 became a thing, it had crawled a very nuanced set of websites, this is what I mean. You basically curate where it sources data from.

Yeah, I think RAG is the idea that will lead us there, though its a little complicated, because for some subjects, say Computer Science, you need a little more than just "This is Hello World in Go" you might need to understand not just Go syntax on the fly, but more CS nuances that are not covered in one single simple document. The idea being having a model that runs fully locally on a phone or laptop with minimal resources. On the other hand, I can also see smaller models talking to larger models that are cheaper to run in the cloud. I am wondering if this is the approach Apple might take with Siri, specifically in order to retain user privacy as much as possible.

I remember reading tht hallucination is still a problem even with perfect context. You build a theoretical perfect RAG, give the LLM the exact correct information, and it will still make mistakes surprisingly often.

I think the idea is to train a small, minimal LLM thinking model that can run on edge devices, but that has very little knowledge embedded in its weights, and so performs a sort of RAG to Encylopedia Britannica to ground answers to user queries.

Log Base 2 of 3 = ~1.5849625, so that's the limit to how well you can pack three-state values into bits of data.

For something more practical, you can pack five three-state values within a byte because 3^5 = 243, which is smaller than 256. To unpack, you divide and modulo by 3 five separate times. This encodes data in bytes at 1.6 bits per symbol.

But the packing of 5 symbols into a byte was not done here. Instead, they packed 4 symbols into a byte to reduce computational complexity (no unpacking needed)

I always hope for "just a bunch of if statements" ... this is not it.

The new model they just released has impressive benchmark results: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T

Except on GSM8K and math...

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?

Rest assured, all the big players (openai, google, deepseek etc) have run countless experiments with 4,3,2,1.58,1 bits, and various sparse factors and shapes. This barrel has been scraped to the bottom

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 1 are trivial, just a mux for identity and zero. But because floats are sign-magnitude, multiply by -1 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?

We have faster memory, it's just all used in data center cards you can't buy (and can't afford to buy).

AMD actually used HBM2 memory in their Radeon VII card back in 2019 (!!) for $700. It had 16 GB of HBM2 memory with 1 TB/s throughput.

The RTX 5080 in conversion l comparison also has 16 GB of VRAM, but was released in 2025 and has 960 GB/s throughput. The RTX 5090 does have an edge at 1.8 TB/s bandwidth and 32 GB of VRAM but it also costs several times more. Imagine if GPUs had gone down the path of the Radeon VII.

That being said, the data center cards from both are monstrous.

The Nvidia B200 has 180 GB of VRAM (2x90GB) offering 8.2 TB/s bandwidth (4.1 TB/s x2) released in 2024. It just costs as much as a car, but that doesn't matter, because afaik you can't even buy them individually. I think you need to buy a server system from Nvidia or Dell that will come with like 8 of these and cost you like $600k.

AMD has the Mi series. Eg AMD MI325x. 288 GB of VRAM doing 10 TB/s bandwidth and released in 2024. Same story as Nvidia: buy from an OEM that will sell you a full system with 8x of these (and if you do get your hands on one of these you need a special motherboard for them since they don't do PCIe). Supposedly a lot cheaper than Nvidia, but still probably $250k.

These are not even the latest and greatest for either company. The B300 and Mi355x are even better.

It's a shame about the socket for the Mi series GPUs (and the Nvidia ones too). The Mi200 and Mi250x would be pretty cool to get second-hand. They are 64 GB and 128GB VRAM GPUs, but since they use OAP socket you need the special motherboard to run them. They're from 2021, so in a few years time they will likely be replaced, but as a regular joe you likely can't use them.

The systems exist, you just can't have them, but you can rent them in the cloud at about $2-4 per hour per GPU.

Chip speed has increased faster than memory speed for a long time now, leaving DRAM behind. GDDR was good for awhile but is no longer sufficient. HBM is what's used now.

The last logical step of this process would be figuring out how to mix the CPU transistors with the RAM capacitors on the same chip as opposed to merely stacking separate chips on the same package.

A related stopgap is the AI startup (forget which) making accelerators on giant chips full of SRAM. Not a cost effective approach outside of ML.

It might be advantageous to have a different memory structure altogether, bespoke to the specific task.

For larger contexts, the bottleneck is probably token prefill instead of memory bandwidth. Supposedly prefill is faster on the M5+ GPUs, but still a big hurdle for pre-M5 chips.

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.

It's scary, without the em dashes, and the rapid fire commenting of the account - who would ever realize this is a bot? Two easy to fix things, and after that it'd be very difficult to tell that this is a bot.

It's not a question of if there are other bots out there, but only what % of comments on HN right now and elsewhere are bot generated. That number is only going to increase if nothing is done.

Looks like gradual disempowerment is already happening - the minority of humans who are capable of spotting AI content are losing the struggle for attention on all major social networks

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?

Yeah because THAT won’t confuse the average reader.

They seem to be getting better or more powerful. The newer Intel Panther lakes and AMD Ryzen are over 50 TOPS now, IIRC

I assume that theoretically, 1-bit models could be most efficient because modern models switched from 32 bit to 16 bit to 8 bit per parameter (without quantization).

> quantized a 100B parameter model to 1 trit

I had the same question, after some debates with Chatgpt, it's not the "quantize" for post-training we often witness these days, you have to use 1 trit in the beginning since pre-train.