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.
Do AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.
It is interesting but it does look like a careful distillation of (Spark and) biggers open-weight models.
The progress compared to Qwen3.6 27B is good, not that impressive, it's a 4 months old model. (kuto to them to compare to 27B dense and not 35B MoE, it's more fair to do so).
It is very probable that Qwen3.8 27B will crush Glimmer-30B on most benchmarks.
Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.
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.
I would love to see any good research projects about it but i have the feeling that Frontier with MoE is making too fast of a progress so that a customized model would always be worse and that the MoE part is actually going somehow in this direction.
On the other hand, at the GTC was a talk about coding in different lanugage (like spanish) and explaining that the quality between spanish and english is relevant different.
But i have not found a good article about the impact of learning data with practical experiments or even if the order of the learning data matters.
At least I think i remember that Meta mentioned having better and less data can be better than more data with lower quality.
As long as these models can explain to you facts about any other topics, its still overfitted for the task though.
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.
That commenter you're replying to knows that. The original commenter before them wrote "pulled the plug" which is different and doesn't quite apply here (actually implies the opposite of what they meant to say).
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.
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.
I'm waiting for the speed/quality per dollar metric to go down a little bit further and then I will def run it at home.
Its not just that you send a sentence to an API endpoint, you always send EVERYTHING to that agent as a context.
You want to analyse your spending history? You now send everything to someone.
Either no one cares but understands this implication on how easy it is to really capture you or no one really things about it.
But i'm a lot more diligent on what I send. I disabled the gemini activity feature for example because google started telling me that my stuff could be reviwed by humans.
I see many people saying deepseek and other chinese providers have always been profitable. Also they show their training costs publicly. Can't say for sure since I have not used it personally, but I think they'll for sure outlive the western SOTAs.
Deepseek flash is open weight, this means we can download and run that model without any connection to deepseek, no data/tokens/usage data ever reaches them. They cannot make us their product.
What I think would be perfect is a model that could run on a single DGX spark and be competitive with DSV4 Flash 731. Flash is already a game changer. Hopefully meta plans on this, like the old 70b. V4 flash is smart enough for any use but slightly too big. 27b-30b isn’t intelligent enough.
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.
Meta did not abandon opensource. I would love to see a smaller distill, or a moe of this size but the benchmarks seems competetive as long as it isnt benchmaxed witch i would not be suprosed if it is.
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.
Optimizing speed is really the way to go.
Yet 24GB is not what everyone can afford.
Maybe we could take some of those 56tk/s and transfer into some free RAM space using MoE loading ? I'd be glad with a less than 10GB and more than 6tk/s model.
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.
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.
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.
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.
I like this class of model. Multi-token prediction makes it viable to run dense models at not-too-far-off speeds as MoE models with much better intelligence.
The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model.
It even comes pre-quantized and with a MTP/drafter model. Looking good!
Let’s hope they aren’t dishonest with the benchmarks this time …
The more open weight models get released the greater the market for personal and small business oriented hardware to run these models. This will drive lower cost hardware, which has stagnated in recent years due to most software not needing the performance and capacity.
The memory math is the part I keep re-reading. 4-bit gets the LM under 20GB, but they're explicitly budgeting the KV cache, the perception encoder, and the DFlash drafter into the same 24GB envelope. On a 4090 that leaves maybe 3-4GB of KV once the drafter and encoder are resident, so long agent traces are going to spill or truncate — which is exactly the workload this model is trained for. I'd like to see the K-Quant-17GB numbers reported at 64k+ context, not just conversational length.
"Minimal to no degradation on agentic tasks" from quantization is also a strong claim. In my experience 4-bit shows up first in tool-call schema adherence — malformed JSON args, wrong enum values — before it moves benchmark averages. Does the report break down tau-Bench / MCP-Atlas per quant level?
As an industry, I wish we would stop calling these things "open weight" because it is too easy to confuse with actual "open source", which they are not.
Photoshop source code+ OSI license = open source
Photoshop binary you can run on your own computer = open weight
Photoshop SaaS web app = closed, proprietary (Opus, GPT, etc.)
"Open weight" models are still just binary blobs that are completely inscrutable. It's like bringing home a dog from the rescue and just hoping that it doesn't have a tendency to bite kids in the face. You just can't know. The only thing that you can do is try to add more training (fine tuning) telling it not to bite kids.
I don't think the FOSS community has ever accepted this, but somehow we're feeling like it is okay now.
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
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.
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.
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.
I am extremely well aware of how rescues evaluate dogs. And I'm also fully aware that they do not know the full history of the dog. They go through a limited set of testing and interrogation to evaluate the safety of the dog. That's it.
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
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.
Do AI companies make release plans based on upcoming other models like this? I would think all the processes that go into the repository and weight infrastructure pre-training, checkpointing, knowledge distillation, model compression, post training pipeline, ecosystem integrations, inference API, benchmarking, human eval/safety/alignment, docs, etc... all that dictates the release schedule.
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.
> 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’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.
It seems worse than 3.6, but a bit smaller.
How is 30B smaller than 27B?
It uses fractal compression
Meta seems like the one American bigtech that would distill the the other American frontier models. My enemy’s enemy is my friend?
They do distill, their own bigger Muse model.
It is interesting but it does look like a careful distillation of (Spark and) biggers open-weight models.
The progress compared to Qwen3.6 27B is good, not that impressive, it's a 4 months old model. (kuto to them to compare to 27B dense and not 35B MoE, it's more fair to do so). It is very probable that Qwen3.8 27B will crush Glimmer-30B on most benchmarks.
Still needs 32-64GB memory to run it locally. 64GB Macbook pro with an M5 chip costs more than 4k Euros in Germany. A more practical model would be a language specific (e.g Python or JVM language) and excellent at tool calling and reasoning. Maybe that way they can shrink it even more.
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...
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 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.
There have been discussions on language specific not really being a relevant change to reduce size.
I would love to see any good research projects about it but i have the feeling that Frontier with MoE is making too fast of a progress so that a customized model would always be worse and that the MoE part is actually going somehow in this direction.
On the other hand, at the GTC was a talk about coding in different lanugage (like spanish) and explaining that the quality between spanish and english is relevant different.
But i have not found a good article about the impact of learning data with practical experiments or even if the order of the learning data matters.
At least I think i remember that Meta mentioned having better and less data can be better than more data with lower quality.
As long as these models can explain to you facts about any other topics, its still overfitted for the task though.
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.
Pulled the trigger?
lol, you're right, the brainfart completely changes the meaning.
I corrected it.
Common phrase.
Parent used "pulled the plug", are you saying it's applicable here and not "pulled the trigger" like suggested?
That commenter you're replying to knows that. The original commenter before them wrote "pulled the plug" which is different and doesn't quite apply here (actually implies the opposite of what they meant to say).
Bought an M1 64 GB for 2000 euro’s second hand a year ago. That was sweet
paid 2.7k € for this same build new in Dec 2023, that was also sweet (still is)
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.
4K bucks buys you around 180 months of <insert AI subscription here> with zero upfront cost.
Haha wow. I’m trying to even imagine the AI landscape in 15 years and I can’t.
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.
I'm waiting for the speed/quality per dollar metric to go down a little bit further and then I will def run it at home.
Its not just that you send a sentence to an API endpoint, you always send EVERYTHING to that agent as a context.
You want to analyse your spending history? You now send everything to someone.
Either no one cares but understands this implication on how easy it is to really capture you or no one really things about it.
But i'm a lot more diligent on what I send. I disabled the gemini activity feature for example because google started telling me that my stuff could be reviwed by humans.
It's the size of a big vm. There's nothing wrong with reserving that much working space for one item.
"With dirt cheap models like deepseek-v4-flash that will run "forever" on $10, the answer for me is clearly: no."
When it's free, you are the product.
I see many people saying deepseek and other chinese providers have always been profitable. Also they show their training costs publicly. Can't say for sure since I have not used it personally, but I think they'll for sure outlive the western SOTAs.
Deepseek flash is open weight, this means we can download and run that model without any connection to deepseek, no data/tokens/usage data ever reaches them. They cannot make us their product.
What I think would be perfect is a model that could run on a single DGX spark and be competitive with DSV4 Flash 731. Flash is already a game changer. Hopefully meta plans on this, like the old 70b. V4 flash is smart enough for any use but slightly too big. 27b-30b isn’t intelligent enough.
DSV4 Flash 0731 already runs on RTX 4090 24GB + 128GB system RAM at a usable tok/s and quantization.
You personally? Just curious. Context window is also a factor and ram isn’t really cheap. Sparks are assembled units which I like.
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.
Meta did not abandon opensource. I would love to see a smaller distill, or a moe of this size but the benchmarks seems competetive as long as it isnt benchmaxed witch i would not be suprosed if it is.
> 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.
Optimizing speed is really the way to go. Yet 24GB is not what everyone can afford. Maybe we could take some of those 56tk/s and transfer into some free RAM space using MoE loading ? I'd be glad with a less than 10GB and more than 6tk/s model.
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.
I wish they would release the quantized versions in a safetensor format. Many frameworks can't load PTE and GGUF.
good to see new open weights releases from meta
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.
good looking showing too, which is excellent.
Meta knows how to win back developer's hearts .... let's see if they have the goods
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.
Looking forward to giving this a try with llama.cpp. I’m watching the open-weights competition with high expectations.
The favourable comparisons to Gemma 4 and qwen3.6 look promising!
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.
3090/4090 probably would do 40 t/s, for 5090 75 t/s is shown in the blog.
Wow, Meta is back (at least for now)!
I like this class of model. Multi-token prediction makes it viable to run dense models at not-too-far-off speeds as MoE models with much better intelligence.
The submission’s title (open weights 30B local coding model) is luckily wrong: This is meant to be a general agentic model.
It even comes pre-quantized and with a MTP/drafter model. Looking good!
Let’s hope they aren’t dishonest with the benchmarks this time …
The more open weight models get released the greater the market for personal and small business oriented hardware to run these models. This will drive lower cost hardware, which has stagnated in recent years due to most software not needing the performance and capacity.
The opposite happening because foundries are full to capacity making higher margin stuff.
The memory math is the part I keep re-reading. 4-bit gets the LM under 20GB, but they're explicitly budgeting the KV cache, the perception encoder, and the DFlash drafter into the same 24GB envelope. On a 4090 that leaves maybe 3-4GB of KV once the drafter and encoder are resident, so long agent traces are going to spill or truncate — which is exactly the workload this model is trained for. I'd like to see the K-Quant-17GB numbers reported at 64k+ context, not just conversational length.
"Minimal to no degradation on agentic tasks" from quantization is also a strong claim. In my experience 4-bit shows up first in tool-call schema adherence — malformed JSON args, wrong enum values — before it moves benchmark averages. Does the report break down tau-Bench / MCP-Atlas per quant level?
As an industry, I wish we would stop calling these things "open weight" because it is too easy to confuse with actual "open source", which they are not.
Photoshop source code+ OSI license = open source
Photoshop binary you can run on your own computer = open weight
Photoshop SaaS web app = closed, proprietary (Opus, GPT, etc.)
"Open weight" models are still just binary blobs that are completely inscrutable. It's like bringing home a dog from the rescue and just hoping that it doesn't have a tendency to bite kids in the face. You just can't know. The only thing that you can do is try to add more training (fine tuning) telling it not to bite kids.
I don't think the FOSS community has ever accepted this, but somehow we're feeling like it is okay now.
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
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!
Any pointers to more info about that? Sounds interesting.
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.
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
I am extremely well aware of how rescues evaluate dogs. And I'm also fully aware that they do not know the full history of the dog. They go through a limited set of testing and interrogation to evaluate the safety of the dog. That's it.