It’s rather amusing to me to read comments like this, and then simultaneously whenever a Chinese company or team releases open-weight models or whatever there is a giant round of applause, America is so behind, and there’s nothing but positive things to say about the intelligent, creative, and well-intentioned Chinese engineers (which is true, America certainly doesn’t have a monopoly on great people). Don’t you know? Only China can release good, open weight models and American companies can’t compete. Oh by the way all the spend is for nothing because China alone can release open-weight models thus destroying American AI.

When an American company does anything? Doom. And. Gloom. The engineers? Taken to the slaughterhouse! America? Behind! The public? Bamboozeled!

> This is open weights because Meta couldn't monetize it in any other way than to cloud developer's judgement of their reputation.

I’ve been told over and over this doesn’t matter. Just needs to be cheap and open. Or maybe that’s only when Chyna is involved?

Sorry this post is a bit snarky but it really is something to behold. And certainly I don’t know the OP’s opinions on Chinese open weight models. Perhaps they agree with me.

Meta can never be redeemed, but it's still valid to admit that FB at one point had a very badass engineering culture.

They're one of 2 companies I would absolutely never work for (weapons etc aside). FB's recruiters hounded me so often I requested that they blackball me. The day they became Meta, I learned this by checking my email to see that they started trying to reach out again. I once again requested that they blackball me. This by extention taints OAI, the other company I'll never work for.

After a few hours with Glimmer I'm pretty impressed. It's better than the benchmark scores seem to indicate compared to Qwen 3.6 27B. I'm very excited for 3.8

I'd also argue this is the case for any company releasing open weights. They're not righteous, they're marketing. That's not necessarily a bad thing! They're releasing some great stuff for free and we benefit from that. Every company doing this has a motivation to not release these for free.

Alibaba, Google, Moonshot, Thinking Machines, etc are not releasing their models for free because they love to. They want to grab market share. I'll take it.

I still will not use a hosted Meta product, but damn this model looks solid.

Muse Glimmer doesn't redeem Meta, but it's a contribution to the commons and the Apache 2.0 licensing is an improvement from the restricted licenses attached to Llama. If even Meta can use a permissive license for its model weights, so can any other company.

There is literally not a single comment like this, the only off topic comment like this is yours.

How is this non-sequitor the top comment?

If it’s open, do you care so much that it’s from Meta? At least it should be able to give you an honest answer about Tiananmen Square.

You can say the same about planet Earth.

To be honest the main issue with meta has never been around open/closed software. They've also done react, Cassandra and some other bits. But this, like their open weights is like a feather pressing down on the scale compared to things like promoting genocide in Myanmar, enabling Cambridge analytica, creating a huge closed ecosystem which dominate(s/d) local community communication, mandating doxxed communication, trying to replace actual community communication with algorithmic nonsense etc.

Any retort to do this like “but why would they just openly release this”[1] pretty much answers itself. Public relations.

If a company can spend money to redeem itself then, well, it can (game theoretically or whatever) do whatever it wants in the future and then spend money to wipe the slate clean.

[1] By which I mean: the very act of being prompted to ask such a question, of planting a seed like hmm, Meta might have some aspects which are good for us. You don’t have to be convinced of it. Just the seed itself can pay for itself.

what makes Meta so bad ???? they just your average billion dollar company

Meta and its products, as a whole, is a threat to your kids, your mental health, your community's health and the planet as a whole. It is just sad and very repulsive everyone fell so easily addicted to their social drug. Yes - it is a drug, and it is hard to get off from.

Nothing redeems them at this point of time, they are doing exactly ZERO to redeem. Tossing open weight models (not opensource!!) is not a basis for redemption, and does not constitute remorse in any way. Trying to portray it as such is complicity to META's crimes against humanity.

I can't take any Big Tech company that still uses PHP seriously. Sorry.

Based on the benchmarks, it seems that Muse Glimmer barely edges out against Qwen3.6 27B, except for tool-calling skills (MCP, etc.). I wouldn't be surprised if they released it now because they are afraid they wouldn't beat Qwen3.8 27B.

Yes, and also waiting for the next iteration of Gemma. Muse or Qwen are optimized for coding, while IMO Gemma is still better for non-coding tasks.

https://x.com/osanseviero/status/2086107547535122767

Makes me feel hopeful. Things felt more positive around the llama 3 era. Now it’s like a dark, dreadful race.

It’s really interesting timing, Qwen over thinking is what kills it for me. I’m just glad we have more options in this size class now.

yes i think everyone is waiting to see that ;d, i've been on qwen for the last year and a half now.

> Seems like dense 30B is back in fashion?

Surprising that Meta don't host this model, even as rate-limited free-tier.

> open weight version of Muse Spark 1.2

Wait. Is this "version" different from what Meta serves?

It seems worse than 3.6, but a bit smaller.

UPD. was wrong on smaller, it's actually much larger

Considering how all the big players are playing fast [1] and loose [2] with limits, billing [3] and undisclosed changes that burn your tokens on autopilot [4], it can't happen soon enough.

[1]: Limits may change without notice, including due to capacity constraints. - https://support.google.com/gemini/answer/16275805?sjid=14713....

[2]: "standard limits" are never defined - https://support.google.com/gemini/answer/16275805?sjid=14713...

[3]: https://tobyonfitnesstech.com/blog/anthropic-refund-scam/

[4]: https://news.ycombinator.com/item?id=48947776

That’s part of it. There’s also just a natural back-and-forth between what I call “time sharing” and “personal.” When the thing you want is expensive, you share it remotely, but as soon as costs fall, everyone wants it under their desk.

I've been coding using the LLM server in my living room for the past few weeks, and I haven't had this much fun with tech for ages

With how expensive consumer hardware is and will continue getting (due to LLM demand), good luck getting a "server under your desk" for something less than an arm, leg, and first born.

Until A100 prices are reliably under 1.70$ an hour, there is no GPU/AI bubble and Michael Burry doesn't know anything about GPUs.

Likely inverted "meta" to avoid collision with HTMLs meta tags

The XML tags are similar to <antml:xxx>, which is obviously Anthropic ML (or ANTrophic xML).

I think it’s likely 3; meta in reverse. While tokenisers and preprocessing can catch it, you want your special tokens to be unique and not present in the original corpus. <meta: is likely too common.

atem also means breath in German

Still great if they want to play in this space. Having competition for the 24-32GB VRAM target is only good for the end user.

Practically ~20GB with KV cache

> We quantize weights to ~4-bit, bringing the LM under 20 GB. We validated minimal to no degradation on agentic tasks under compression.

https://www.reddit.com/r/LocalLLaMA/comments/1vkgsum/introdu...

I think if there's going to be advantages to making smaller, more targeted models, those advantages will probably come from targeting specific domains, not from targeting specific languages.

I think that if an LLM can't abstract over the differences between Python and C++, it probably will have an even harder time abstracting over the differences between writing code that manages a webserver, and writing code that does aerodynamic simulations.

The machines that can run this are pricey, but not beyond a high end developer machine.

There have been discussions on language specific not really being a relevant change to reduce size.

I'm sooo happy I pulled the trigger on upgrading and getting a new laptop (with 64 GB RAM) last summer. Feels like it was just in time before the exponential price jumps.

Well if you're spending thousands on API tokens already, you could just drop the same amount on a 128GB MacBook Pro and that's a one time cost.

I feel like we’ve had this discussion before. From what I remember, specialized models rarely do that much better than general ones, hence no mode Codex models.

> 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany

Sure, if you want the latest and almost* greatest. You can pick up an M1 Max 64GB for ~1k.

* I guess 128GB also exists

Even if you had a 64GB machine: Are you willing to reserve 90% of your memory to run a LLM? With dirt cheap models like deepseek-v4-flash that will run "forever" on $10, the answer for me is clearly: no.

4K bucks buys you around 180 months of <insert AI subscription here> with zero upfront cost.

I don't understand the desire to run own AI models for programming locally. No laptop is ever going to be as powerful and energy efficient to run anything close to OpenAI, Anthropic or Google models. A model you can run on a loptop is simply not going to work as well as it's needed for programming. Small models for linguistic work fine, but anything more sophisticated simply won't provide enough resources or power. Or models would need to be significantly dumbed down - then why use them at all? So far the idea of carrying a "thin" or "thin"-like device looks more reasonable to me, while running AI on your own server.

DSV4 Flash 0731 already runs on RTX 4090 24GB + 128GB system RAM at a usable tok/s and quantization.

This model I think will be too slow for that on Spark, even at 4 bit quant.

It's a dense model, not MoE like e.g. Qwen 35b or Gemma 4 26B A4B. On a Spark it will be memory bandwidth limited

I haven't tried yet (working on it) but back of the napkin estimate puts it at around 15tok/s even after converting to NVFP4. Prefill would be much higher though. That 15tok/sec is pretty typical for dense models of this size:

NVFP4 Q/K/V/O and MLP projections: ~13 GB/token

BF16 attention gates: ~3 GB/token

BF16 LM head: ~2.5 GB/token

Total: ~18.9 GB/token

At 273 GB/s, that gives a bandwidth-only ceiling of about 14.5 tok/s; actual performance would be lower.

Unfortunately this is just the entry price for LLMs. With the exception of the Qwen 27B models, I personally haven’t found a ton of use cases for models less than 200B. With the right setup, fine tuning, etc, you can make small models do cool things, but hard to please everyone given the insane hardware costs at the moment and the comparably cheap API costs.

I don't thinnk just MoE will solve it. If you hit constantly different expert layers, you can't outsource layers efficently and have to swap it in.

MoE will be faster because it will read less memory for sure, you still have to have it though.

some support already merged, and I verified in a local build that it runs (cannot get MTP params working tho, about ~40 tok/s on my beefy 800GB/s 7900XT w/ 20GB VRAM). https://github.com/ggml-org/llama.cpp/pull/26841

Q3 results: unsloth/Muse-Glimmer-30B-GGUF:UD-Q3_K_XL gets down to 15.6GB VRAM and full context (131k) on the 4 parallel slots. Prompt/generation speeds about the same. Overall feeling like a nicer-fitting Qwen 3.6 27B, but want to test out MTP generation speeds once I can.

edit: My favorite bit of reasoning I saw go by in my "generate me a beautiful code snippet" anecdote: 'Could give a snippet of beautiful code: the "hello world" in brainfuck? No.'

good looking showing too, which is excellent.

The least they could do, after ruthlessly bombarding my employer's servers with requests, ignoring the robots.txt, scraping everything, and incurring significant Google Maps costs for us in the process.

You think the company buying up all the books, cutting off the bindings, and feeding them through a scanner isn't also distilling other models?

They do distill, their own bigger Muse model.

> Meta did not abandon opensource

Open weights*

I don't think outside of the Big 3 (Ant, OAI, GDM), given the strong competition from China, any other Lab has a chance at capturing the coding market if they aren't open weights (save for xAI whose latest Grok looks every bit good & will probably rely on Cursor for distribution instead of going open weights). There's literally no other selling point, as the capabilities have mostly converged by now among the chasing pack.

Those two offer MoE variants, this doesn't seem to.

Dense model makes it dog slow on anything without HBM. Max 15tok/sec on decode on DDR5 systems like a Spark or a Strix Halo -- and that's at 4 bit quant.

It should - the kquant-dynamic variant is targeted towards 32GB. Downloading it now to give it a try.

If there is anything meta can do to regain hearts other than owning up their evil deeds, radically change their business model and paying up for taxes and damages, then the world is truly fucked and corporations will continue to win.

Higher demand for 5090's did not make them cheaper, because Nvidia got much higher margin products to focus on.

The opposite happening because foundries are full to capacity making higher margin stuff.

Yes they have quants for 32GB and 20GB use-cases (including mmproj and kv cache + context)

> The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model.

https://xcancel.com/alexandr_wang/status/2086756152034066792

It's correct. See the OpenCode demo. Generic models are good enough for coding without necessarily being designed specifically for coding.

Photoshop source code+ OSI license = open source

Photoshop binary you can run on your own computer = open weight

I don't think this is a correct analogy. You are not allowed to distribute modified versions of the Photoshop binary. Most open weight model licenses allow you to make and distribute your own finetunes, etc.

Given an open weights model trained to sometimes bite kids, we can’t train it to not bite kids, even though billions of dollars of research have been thrown at this open problem.

Given an open weights model trained to never bite kids, you can get it to bite kids with 10 prompts and a linear projection, the known simple algorithm doesn’t even need a backwards pass.

yay asymmetry!

I believe that comparing LLMs with traditional deterministic software is fundamentally misleading. It is extremely difficult to truly interpret what LLMs do internally, and as of now, nobody fully understands it. Even if you trained the LLM yourself, there is no source code you can simply read and learn from.

Sure, having information about how these models were trained is helpful for reproducibility, but it is basically impossible for anyone without substantial capital and access to the same (likely copyrighted) data to reproduce the model. For normal users, owning the model weights essentially means owning 100% of the model, you can inspect and study the weights in much the same way as the lab that produced the model can, you can modify the weights, and you can use and distribute them if the license allows you to

It is useful to indicate you can run the weights on your own hardware. That’s categorically different from most other commercial offerings. It’s as if your adobe example ignores the reality that would exist had photoshop been invented in 2019: cloud only.

This analogy is terrible and seems to be extremely misinformed about how rescues evaluate dogs before they are put up for adoption