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Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

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631–640 of 678 posts

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#631
post #272

Remember when we needed 200 servers for an enterprise website because Apache used one process or thread per connection - and Nginx collapsed that into a single box overnight? That moment for LLMs is near. It’s going to move us from the big iron era of AI to small portable brains. Nature has already proved it’s possible with 20 watts and very little heat generation. And I think the data center buildout will end in car…

Side note! Nginx was by no means the first web server to use a non-forking mechanism, nor the first open source web server to do so. Certainly Zeus (which was closed source) was earlier and very useful in this sort of application, and so was thttpd (open source, still exists as Merecat). I used thttpd quite a bit for single box applications and at one of my employers, nginx replaced a mixed strategy with Zeus, Apache…

Also lighttpd! Released in 2003, one year before nginx (2004)

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#635
post #459
post #294

Earlier quoted context omitted.

brains do it with 20 watts because theyre analog. llms require massive amounts of power and this isnt changing any time soon without a breakthrough

There are arguments that the brain is quantum, as in parts of it locally using quantum effects. Which if true, might make a counter-argument, as there will be bigger data centers needed if the goal is to simulate the brain classically. On the other side, advancement in quantum computers would make current LLM inference much faster. Because of the extreme cooling needed, i dont think the energy demand would become les…

Can you give me some pointers?

Last I heard researches simulated an entire brain of a fruitfly with just a classic neuron approach [0] and if I recall it worked great.

I know a human brain is many orders of magnitude more complex, and that there are some birds that use quantum navigation - so it's certainly not impossible by any stretch, though I am quite interested in this statement since it's the first I hear about it.

[0]: https://flybrain.app/

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#636
post #5

Will be interesting to see how Qwen3.8 27B compares against this once it releases this week. Seems like dense 30B is back in fashion? EDIT: An open weight version of Muse Spark 1.2 is going to be released as well: https://x.com/alexandr_wang/status/2086756152034066792 https://xcancel.com/alexandr_wang/status/2086756152034066792

Well, it has to, since even the MoE models can't really hold a conversation.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#637
post #571

Earlier quoted context omitted.

If you are willing and not too busy, What model do you use and what is your cost? (If using subscription would you be able to check with 'npx ccusage').

I'd also be really curious about the cost to run something like this, and what things you think it's particularly helpful for?

I run this on a side of the Claude Pro subscription that I use for other purposes. My main motivation was root cause analysis of production issues. I have a solo project and unfortunately my mental state has been degrading over the last years. I would avoid looking at production issues, because I didn't have the energy to focus on the investigation. So I automated this, setup the loop, setup metrics/logs access for Claude to use and now whenever something goes bad, I have a single report that I can act on easily, and if I don't, it will ping me in a way that's not spammy like automated alerts. But I'm finding more uses for it.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#638

Remember when we needed 200 servers for an enterprise website because Apache used one process or thread per connection - and Nginx collapsed that into a single box overnight? That moment for LLMs is near. It’s going to move us from the big iron era of AI to small portable brains. Nature has already proved it’s possible with 20 watts and very little heat generation. And I think the data center buildout will end in car…

> And I think the data center buildout will end in carnage.

Isn’t it more likely that they will still all be used to the max? I don’t see how at any rate we will be going “alright, that’s enough intelligence for now”

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#639
post #624
post #585

Earlier quoted context omitted.

> So far everyone seems to be consistently GPU-poor, despite the huge buildout That is seemingly not the case. The buildout is actually slow; almost nothing of these giant projects has been completed. Nobody will say how much of anything they have actually finished. And Nvidia have made huge, huge buy-and-hold deals for GPUs that do not have data centres to go into. Everyone is GPU poor because stuff hasn't been fini…

If I understand correctly, you're saying people are compute-poor but not necessarily GPU-poor because there's a lot of GPUs out there but nowhere to plug them into? If so, I'm not sure that distinction matters to the GP's point that there is too much demand to call this an oversupply. I also doubt we can estimate the level of demand based on a single deal between Anthropic and SpaceX (despite which, note, Claude stil…

> If I understand correctly, you're saying people are compute-poor but not necessarily GPU-poor because there's a lot of GPUs out there but nowhere to plug them into? If so, I'm not sure that distinction matters to the GP's point that there is too much demand to call this an oversupply.

It's a distinction without a difference if your issue is getting hold of a GPU.

But there's a significance to it if you are trying to use demand for a GPU as a proxy for demand for AI. That is where the industry is making serious mistakes.

> I am not sure Microsoft thinks there will be an oversupply either

https://finance.yahoo.com/news/microsoft-ceo-says-overbuild-...

The rest of your comment I am not going to address because it's kind of unfalsifiable. Hypotheses about what AI might be able to do in principle aren't all that useful when talking about even medium-to-long-term demand for what LLMs and GANs can provably do now.

Re: Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

#640
post #459
post #294

Earlier quoted context omitted.

brains do it with 20 watts because theyre analog. llms require massive amounts of power and this isnt changing any time soon without a breakthrough

There are arguments that the brain is quantum, as in parts of it locally using quantum effects. Which if true, might make a counter-argument, as there will be bigger data centers needed if the goal is to simulate the brain classically. On the other side, advancement in quantum computers would make current LLM inference much faster. Because of the extreme cooling needed, i dont think the energy demand would become les…

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