Comment by johndough
10 hours ago
The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/
The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds).
The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second).
At the high end, there are servers with multiple GPUs with lots of VRAM or multiple chained Macs or Strix Halo mini PCs.
The key enabler here is that the models are MoE (Mixture of Experts), which means that only a small(ish) part of the model is required to compute the next token. In this case, there are 32B active parameters, which is about 16GB at 4 bit per parameter. This only leaves the question of how to get those 16GB to the processor as fast as possible.
You can run AI models on unified/shared memory specifically on Windows, not Linux (unfortunately). It uses the same memory sharing system that Microsoft originally had built for gaming when a game would run out of vram. If you:
- have an i5 or better or equivalent manufactured within the last 5-7 years
- have an nvidia consumer gaming GPU (RTX 3000 series or better) with at least 8 GB vram
- have at least 32 GB system ram (tested with DDR4 on my end)
- build llama-cpp yourself with every compiler optimization flag possible
- pair it with a MoE model compatible with your unified memory amount
- and configure MoE offload to the CPU to reduce memory pressure on the GPU
then you can honestly get to about 85-90% of cloud AI capability totally on-device, depending on what program you interface with the model.
And here's the shocking idea: those system specs can be met by an off the shelf gaming computer from, for example, Best Buy or Costco today and right now. You can literally buy a CyberPower or iBuyPower model, again for example, download the source, run the compilation, and have that level of AI inference available to you.
Now, the reason why it won't work on Linux is that the Linux kernel and Linux distros both leave that unified memory capability up to the GPU driver to implement. Which Nvidia hasn't done yet. You can code it somewhat into source code, but it's still super unstable and flaky from what I've read.
(In fact, that lack of unified memory tech on Linux is probably why everyone feels the need to build all these data centers everywhere.)
Its often pointed out in the first sentence of a comment how a model can be run at home, then (maybe) towards the end of the comment it’s mentioned how it’s quantized.
Back when 4k movies needed expensive hardware, no one was saying they could play 4k on a home system, then later mentioning they actually scaled down the resolution to make it possible.
The degree of quality loss is not often characterized. Which makes sense because it’s not easy to fully quantify quality loss with a few simple benchmarks.
By the time it’s quantized to 4 bits, 2 bits or whatever, does anyone really have an idea of how much they’ve gained vs just running a model that is sized more appropriately for their hardware, but not lobotomized?
Didn't this paper demonstrate that you only need 1.58 bits to be equivalent to 16 bits in performance?
https://arxiv.org/abs/2402.17764
> ...Back when 4k movies needed expensive hardware, no one was saying they could play 4k on a home system, then later mentioning they actually scaled down the resolution to make it possible. ...
int4 quantization is the original release in this case; it's not been quantized after the fact. It's a bit of a nuisance when running on hardware that doesn't natively support the format (might waste some fraction of memory throughput on padding, specifically on NPU hw that can't do the unpacking on its own) but no one here is reducing quality to make the model fit.
Good point thanks for the clarification.
The broader point remains though which is, “you can run this model as home…” when actually the caveats are potentially substantial.
It would be so incredibly slow…
From my own usage, the former is almost always better than the latter. Because it’s less like a lobotomy and more like a hangover, though I have run some quantized models that seem still drunk.
Any model that I can run in 128 gb in full precision is far inferior to the models that I can just barely get to run after reap + quantization for actually useful work.
I also read a paper a while back about improvements to model performance in contrastive learning when quantization was included during training as a form of perturbation, to try to force the model to reach a smoother loss landscape, it made me wonder if something similar might work for llms, which I think might be what the people over at minimax are doing with m2.1 since they released it in fp8.
In principle, if the model has been effective during its learning at separating and compressing concepts into approximately orthogonal subspaces (and assuming the white box transformer architecture approximates what typical transformers do), quantization should really only impact outliers which are not well characterized during learning.
Interesting.
If this were the case however, why would labs go through the trouble of distilling their smaller models rather than releasing quantized versions of the flagships?
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The level of deceit you're describing is kind of ridiculous. Anybody talking about their specific setup is going to be happy to tell you the model and quant they're running and the speeds they're getting, and if you want to understand the effects of quantization on model quality, it's really easy to spin up a GPU server instance and play around.
> if you want to understand the effects of quantization on model quality, it's really easy to spin up a GPU server instance and play around
Fwiw, not necessarily. I've noticed quantized models have strange and surprising failure modes where everything seems to be working well and then does a death spiral repeating a specific word or completely failing on one task of a handful of similar tasks.
8-bit vs 4-bit can be almost imperceptible or night and day.
This isn't something you'd necessarily see playing around, but when trying to do something specific
Except the parent comment said you can stream the weights from an SSD. The full weights, uncompressed. It takes a little longer (a lot longer), but the model at least works without lossy pre-processing.
> The model absolutely can be run at home. There even is a big community around running large models locally
IMO 1tln parameters and 32bln active seems like a different scale to what most are talking about when they say localLLMs IMO. Totally agree there will be people messing with this, but the real value in localLLMs is that you can actually use them and get value from them with standard consumer hardware. I don't think that's really possible with this model.
Local LLMs are just LLMs people run locally. It's not a definition of size, feature set, or what's most popular. What the "real" value is for local LLMs will depend on each person you ask. The person who runs small local LLMs will tell you the real value is in small models, the person who runs large local LLMs will tell you it's large ones, those who use cloud will say the value is in shared compute, and those who don't like AI will say there is no value in any.
LLMs which the weights aren't available are an example of when it's not local LLMs, not when the model happens to be large.
> LLMs which the weights aren't available are an example of when it's not local LLMs, not when the model happens to be large.
I agree. My point was that most aren't thinking of models this large when they're talking about local LLMs. That's what I said, right? This is supported by the download counts on hf: the most downloaded local models are significantly smaller than 1tln, normally 1 - 12bln.
I'm not sure I understand what point you're trying to make here?
32B active is nothing special, there's local setups that will easily support that. 1T total parameters ultimately requires keeping the bulk of them on SSD. This need not be an issue if there's enough locality in expert choice for any given workload; the "hot" experts will simply be cached in available spare RAM.
When I've measured this myself, I've never seen a medium-to-long task horizon that would have expert locality such that you wouldn't be hitting the SSD constantly to swap layers (not to say it doesn't exist, just that in the literature and in my own empirics, it doesn't seem to be observed in a way you could rely on it for cache performance).
Over any task that has enough prefill input diversity and a decode phase thats more than a few tokens, its at least intuitive that experts activate nearly uniformly in the aggregate, since they're activated per token. This is why when you do something more than bs=1, you see forward passes light up the whole network.
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I never said it was special.
I was trying to correct the record that a lot of people will be using models of this size locally because of the local LLM community.
The most commonly downloaded local LLMs are normally <30b (e.g. https://huggingface.co/unsloth/models?sort=downloads). The things you're saying, especially when combined together, make it not usable by a lot of people in the local LLM community at the moment.
do you guys understand that different experts are loaded PER TOKEN?
I'd take "running at home" to mean running on reasonably available consumer hardware, which your setup is not. You can obviously build custom, but who's actually going to do that? OP's point is valid
How do you split the model between multiple GPUs?
With "only" 32B active params, you don't necessarily need to. We're straying from common home users to serious enthusiasts and professionals but this seems like it would run ok on a workstation with a half terabyte of RAM and a single RTX6000.
But to answer your question directly, tensor parallelism. https://github.com/ggml-org/llama.cpp/discussions/8735 https://docs.vllm.ai/en/latest/configuration/conserving_memo...
>The model absolutely can be run at home.
There is a huge difference between "look I got it to answer the prompt: '1+1='"
and actually using it for anything of value.
I remember early on people bought Macs (or some marketing team was shoveling it), and proposing people could reasonably run the 70B+ models on it.
They were talking about 'look it gave an answer', not 'look this is useful'.
While it was a bit obvious that 'integrated GPU' is not Nvidia VRAM, we did have 1 mac laptop at work that validated this.
Its cool these models are out in the open, but its going to be a decade before people are running them at a useful level locally.
Hear, hear. Even if the model fits, a few tokens per second make no sense. Time is money too.
If I can start an agent and be able to walk away for 8 hours, and be confident it's 'smart' enough to complete a task unattended, that's still useful.
At 3 tk/s, that's still 100-150 pages of a book, give or take.
Maybe for a coding agent, but a daily/weekly report on sensitive info?
If it were 2016 and this technology existed but only in 1 t/s, every company would find a way to extract the most leverage out of it.
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