Microsoft BitNet: inference framework for 1-bit LLMs
1–10 of 36 posts
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#2For anyone that hasn't read the previous papers before the "1.58-bit" part comes from using 3 values (-1, 0, 1) and log2[3]=1.58...
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#3I'm enthusiastic about BitNet and the potential of low-bit LLMs - the papers show impressive perplexity scores matching full-precision models while drastically reducing compute and memory requirements. What's puzzling is we're not seeing any major providers announce plans to leverage this for their flagship models, despite the clear efficiency gains that could theoretically enable much larger architectures. I suspect there might be some hidden engineering challenges around specialized hardware requirements or training stability that aren't fully captured in the academic results, but would love insights from anyone closer to production deployment of these techniques.
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#4I'm enthusiastic about BitNet and the potential of low-bit LLMs - the papers show impressive perplexity scores matching full-precision models while drastically reducing compute and memory requirements. What's puzzling is we're not seeing any major providers announce plans to leverage this for their flagship models, despite the clear efficiency gains that could theoretically enable much larger architectures. I suspect…
I find it a little confusing as well. I wonder if its because so many of these companies have went all in on the "traditional" approach that deviating now seems like a big shift?
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#5No GPU inference support?
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#6No GPU inference support?
> that support fast and lossless inference of 1.58-bit models on CPU (with NPU and GPU support coming next).
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#7Can anyone help me understand how this works without special bitnet precision-specific hardware? Is special hardware unnecessary? Maybe it just doesn't reach the full bitnet potential without it? Or maybe it does, with some fancy tricks? Thanks!
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#8I'm enthusiastic about BitNet and the potential of low-bit LLMs - the papers show impressive perplexity scores matching full-precision models while drastically reducing compute and memory requirements. What's puzzling is we're not seeing any major providers announce plans to leverage this for their flagship models, despite the clear efficiency gains that could theoretically enable much larger architectures. I suspect…
I think that since training must happen on a non-bitnet architecture, tuning towards bitnet is always a downgrade on it's capabilities, so they're not really interested in it.
But maybe they could be if they'd offer cheaper plans, since it's efficiency is relatively good.
I think the real market for this is for local inference.
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#9Can anyone help me understand how this works without special bitnet precision-specific hardware? Is special hardware unnecessary? Maybe it just doesn't reach the full bitnet potential without it? Or maybe it does, with some fancy tricks? Thanks!
I haven't checked this one out yet, but a common trick is using combinations of instructions and data invariants allowing you to work in "lanes".
The easiest example is xor, which can trivially be interpreted as either xoring one large integer or xoring a vector of smaller integers.
Take a look at the SWAR example here [0] as a pretty common/easy example of that technique being good for something in the real world.
Dedicated hardware is almost always better, but you can still get major improvements with a little elbow grease.
Re: Microsoft BitNet: inference framework for 1-bit LLMs
#10Can anyone help me understand how this works without special bitnet precision-specific hardware? Is special hardware unnecessary? Maybe it just doesn't reach the full bitnet potential without it? Or maybe it does, with some fancy tricks? Thanks!
I haven't checked this one out yet, but a common trick is using combinations of instructions and data invariants allowing you to work in "lanes". The easiest example is xor, which can trivially be interpreted as either xoring one large integer or xoring a vector of smaller integers. Take a look at the SWAR example here [0] as a pretty common/easy example of that technique being good for something in the real world. D…
This is extremely easy to implement in-FPGA.