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

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

#351
post #343

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…

Everyone keeps repeating this who doesn’t understand the underlying technology. Small llms are still way more efficiently server on big GPUs. Sharing server capacity takes advantage of the massive parallel throughput and sharing of memory bandwidth. You are sharing the GPUs with thousands of concurrent users.

FWIW it is entirely possible to square the notion that small models will still be hosted on cloud hardware with the idea that the data centre buildout will end in tears.

Many analysts (and Microsoft) think even now that if everything committed gets built there will be considerable oversupply and there is not the revenue to pay for it.

If small models do continue to improve in unusual ways (I think there are limits) then the marginal need for cloud AI compute could fall precipitously beyond current estimates. The marginal need for consumer AI could almost totally collapse if someone makes good progress on very small reasoning and tool-calling models (which is a modestly big if)

The possibility of the data centre boom resembling the Chinese real estate bubble is not inconsiderable.

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

#352
post #118

Earlier quoted context omitted.

You're mixing up sizes of different quants. The 60GB is unquantized, and Qwen's unquantized size is around 54GB. Their sizes as like quantization levels are similar.

From my perspective it doesn't make sense to talk about the number of parameters. What matters is model size in bytes and its performance at that certain size. Meta actually relesed official 4 bit quants in 17GB, but I haven't seen any indication that training was quant-aware, so the quants are not going to have same performance. 3.6 27B has official FP8 quant that AFAIR was trained with quantization awareness. The b…

> but I haven't seen any indication that training was quant-aware

readme on huggingface says they've benchmarked the quants -- for 17GB quant reported 1% avg loss across 15 benchmarks (sadly no breakdown).

I assume that's strong enough signal for QAT. Not just first party quants, but they cared to monitor degradation.

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

#354
Let’s give thanks to all those meta engineers who have been ripped for my heir teams (while sitting right by them) working on manually tagging data. I guess the morale dip paid off in some way? I wish you all well and hope you find some happiness … IYKYK

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

#355
Unsloth has quantized versions uploaded: https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF

The quantized releases often change in the weeks following release as new improvements are discovered, so either use a tool that checks HuggingFace for new versions or manually check back in a few days or weeks to check for improved versions.

Initial reports are good. It hasn't been out long enough for anyone to really test thoroughly, but the people I know who have stable non-public test cases are reporting impressive results compared to even Qwen3.6 27B. That's a good sign that this might not be benchmaxxed (trained to excel at public benchmarks with less impressive performance on general tasks) which has been becoming common with recent releases.

www.reddit.com/r/localllama is a good place to keep up with the details from people who are actually using it. It feels strange to recommend a subreddit over Hacker News, but on this topic the /r/localllama threads are much more on topic right now if you're looking for information about the model.

There are some initial reports that even the 2-bit quantization is looking somewhat usable. That might make it small enough to squeeze into 16GB GPUs. I'd take those reports with a grain of salt because early tests are often optimistic and I've yet to see good results from anything 3-bit or less, but it should be fun to experiment with.

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

#357
Pelican, rendered by Muse Glimmer on my Mac running LM Studio (with this model release: https://lmstudio.ai/models/muse-glimmer): https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

It has all of the components of a pelican riding a bicycle, though not exactly arranged in the right order!

(For comparison, here are the pelicans I got from Muse Spark 1, 1.1, and 1.2: https://bsky.app/profile/simonwillison.net/post/3mseqv5z4qk2... )

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

#358

Earlier quoted context omitted.

> The problem is that when there are any sides, they spend the top 100 comments rehashing the same arguments, often over a political bugbear or web design faux pas. This is a problem with any upvote/downvote based site, in my experience. It only takes a couple people who are highly engaged and who have a lot of free time to refresh the comment section and downvote everyone who disagrees with them. Some times I’ll wri…

the downvoting rings ( or bots ) are very active here. I think they're mostly bots, but who knows i guess

I don't think they're bots and I don't even think they're organized.

In my experience with other communities, it only takes 3-5 people with a lot of free time to crush conversations they don't like. On a fast moving comment section a wrongthink comment can be buried into the bottom half of the comments with only a few early downvotes.

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

#359
post #132

Earlier quoted context omitted.

Not to mention all the other ways they can screw you: - Middle of the day, servers busy? Swap to Sonnet while pretending it's still Opus. Many people won't notice, and nobody can prove anything if they suspect. - Middle of the night, server load is light? Put it into extra thinky mode so it burns more tokens to ramp up the bills. Flip the switch where it gets really pedantic about writing lots of extra test cases and…

I find Claude doing a lot of pointless confirmation at night when it will ask me about things that it would normally just do during the day. Frustrating to be like “do X overnight, don’t ask me for input” and come down to find it having worked for a few minutes and then stopped.

Doesn't setting the question auto-continue timeout before you go to bed fix that?

https://code.claude.com/docs/en/tools-reference#question-aut...

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