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I am glad they released it because I think we need a competitive culture of open weights that isn't just geopolitics.

But I have to say, I quite like the way Muse Glimmer thinks and talks. It's a cocky bastard in tone, but it's quite good, and its thinking traces are relatively terse.

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 will also be very interesting to see some direct head to head benchmarks between qwen 3.6 27B (let's say all at Q8 XK quantization, using the GGUF that unsloth publishes as a baseline) vs 3.8 27B. Particularly in tool use, terminal use.

The whole class of what can reasonably fit in a single GPU is an interesting category of LLM, and based on the results I've seen from 3.6 35B A3B and 27B versus what existed a year prior, it seems there's a lot of room for advancement.

I would hope that Qwen 3.8 is better. It's been 4 months, and we've seen almost no progress in this space.

As people have called out, Glimmer appears to be a trade-off rather than a clear winner.

And from what I've been reading, no one is expecting Qwen 3.8's model in this space to be a clear winner, but just slightly and marginally better.

That's a little concerning as DeepSeek v4 Flash proved at it larger sizes there's a ton of room left to compress knowledge.

If we don't see something that's substantially better in the ~30B param space soon - it would appear we might've saturated that size with knowledge.

For so many non-coding workflows, tool calling is more important.

Qwen3.6 27B has really punched above its weight for a long time. It’s shockingly good for its size. Very excited to see what 3.8 can do.

Qwen3.6 27B is the go-to medium sized model for coding, so beating it is not a small achievement

I am working on a project where we have to classify customer calls into more than 10 categories. As the client wants everything locally I tried a few local LLMs. Gemma turned out to be the best model for this task. The classification accuracy is impressive, and the client is happy that I am using an American model.

You can partially tell by the tokeniser; which gives you some hint into the training corpus mix.

</div> is four Gemma4 tokens, but one Qwen3.6 token.

I've been using Qwen3.6 35B A3B, and with reasoning turned on, I'd say 2/3 (give or take) of the tokens for a response are thinking tokens. Which at 70+ tps locally, that isn't that awful. I run an 80k context across 4-10 "agents" for my solo TTRPG, where Qwen is the GM, each NPC at a location, the director, and the narrator.

Each turn is about 45-60 seconds to generate all of the various responses. The GM and director have reasoning on, and the NPCs/Location/Narrator do not.

It's a fairly good "engine" for that. I'm not sure how a denser Qwen would do here regarding speed.

Just to play devil’s advocate: you can’t compare Qwen to a (proprietary/closed source) hosted model and deduce that Qwen is overthinking, as Qwen gives you the full reasoning/thinking trace while all the proprietary models now give you only a summary “to prevent distillation”, making it hard to properly compare apples to apples here.

Qwen thinking is really good in Mandarin; and probably natively trained the most there.

Try a system prompt requiring it to think in Mandarin, while still delivering the response in the user’s language.

Disable thinking? I think many harnesses disable thinking on Qwen anyways because it interferes with tool calling.

Llamscpp provides reasoning budget and message. You can use the message to redirect it.

Once you get the agent and message consistent,itll keep moving.

I feel you. not sure if the "Glimmer" (of hope) branding is intentional to capture this vibe

I'd be skeptical w.r.t. "by all metrics".

Qwen3.6 is a definitive, significant downgrade from Qwen3.5 for creative writing and prose for example. Yes, it's better at agentic and coding, but it regresses in many non-coding areas compared to Qwen3.5.

Of course, I do expect the 3.8 ones to perform better for agentic coding.

One thing I would caution is staying out of the prediction market like this.

Tech tends to get boring when you judge current products against the hypothetical capabilities of unannounced products that may never ship. It's like comparing Nikon cameras against Canon camera rumours, or comparing iPhones against unannounced and therefore largely imaginary Samsungs.

- If they do a Qwen 3.8 35B A3B (and I hope they do because I love the 3.6 version)

- and if it beats 3.6 27B by all metrics

… then the local open weights world will be a better place.

But they have said nothing about it and they dropped several weight classes for 3.6, so who is to say they won't drop the 35B? And even if they don't, this is a tall order; why would the MoE tradeoffs no longer be apparent? (Again, I really like both the Qwen and Gemma MoEs)

FWIW I am enjoying testing Muse Glimmer — it's really quite impressive on chat, has nice terse and even amusing thinking traces, a bit of brass to it, and I'm hoping it will be good on agentic stuff.

I'll back up your smaller claim, but be specific that it's UD-Q4_K_XL size:

- muse glimmer: 15.9GB

- qwen 3.6 27B: 17.6GB

My video card is so close to its limit that these GB thresholds are mattering too much for me :D

How is 30B smaller than 27B?

> Nginx’s real strength was being able to proxy and cache HTTP using that same mechanism

Fun fact: Igor Sysoev originally wrote mod_accel [1], an acceleration and reverse-proxy caching module for Apache before he made nginx.

I remember using that module in the mid-2000s as a load balancer (and to offload SSL encryption/decryption as it was a CPU-expensive operation).

[1] http://sysoev.ru/en/apache_modules.html

We've barely even started on optimizations like advanced language aware grammars, and specialization routing (dynamically loading fine tunes or seperate weights for specific tasks or languages).

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

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.

It may not make financial sense for someone retired, not into tech, and/or data privacy to host their own LLMs. However if usage of AI in day to day lives continues to increase, I think it will eventually make sense for the majority.

Many tasks suited for AI assistants are background asynchronous tasks. They can run in the downtime where immediate demand is low, keeping overall utilization high enough.

Your argument is similar to those who argue that owning a GPU for gaming doesn't make sense when you can stream from something like GeForce Now. However like with gaming locally (improved latency) there are also benefits to local AI (data privacy).

The power of small models isn't only that you can run them on local hardware. You can also fully own your data and workflow, and choose/fine-tune models for your specific use-case.

for clarity, I'm not agreeing with GP that small models will mean doom for data center projects

> Small llms are still way more efficiently server on big GPUs.

Yes, but the privacy aspect means that for many, many applications slower local will still be preferable to faster remote so long as the actual model performance is the same.

Indeed. Local compute and RAM are a some of the most wasted resource, sitting idle 95 percent of their life. Datacenters flip that ratio.

True, but local setups can run LLM requests in parallel too. In this case efficiency gap is much narrower.

I’m sure you could find this exact same comment regarding technology in 1999.

Absurd indeed. Apache httpd server got MPM support two years before nginx was released.

The researchers who published Attention Is All You Need didn’t have the benefit of the LLMs they birthed. Take a look at the prompt that solved the Cycle Double Cover conjecture, and which has been adapted to achieve breakthroughs in cybersecurity. The field is entering a feedback loop that is leading to exponential innovation. We’re at the beginning of the curve. And right now the big iron data center approach is brute forcing the problem.

This is a statement of nearly pure faith not fact. Which is fine. I have a lot of things I believe based in pure faith. The difference is that I don't state them as if they were fact. Which you appear to be doing here.

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 less.

With AI companies talking about AGI, i sometimes wonder if they really need the machines for serving inference to customers, or they have a formula for computational capacity that could run an AGI, and they just want to reach that level.

And a breakthrough in hardware, specifically.

For me Laguna frequently slightly corrupted text then it would be unable to notice the difference and get stuck making the dumbest conclusions. Thinks like typoed directory or function names. It was a great model other than that, but I ended up just going back to Qwen3.6

Two DGX sparks will run the full Deepseek V4 Flash full. (It is definitely expensive, but relatively easy and compact; and extremely power efficient)

From the Ollama docs for this new model:

Ollama's MLX engine provides state-of-the-art performance on Apple Silicon, with support for DFlash and image input:

ollama run muse-glimmer:30b-mlx

When that article was first published , I started looking into llama.cpp. With the help of an LLM I researched the knobs to turn that made most sense.

Things went from "local models are useless unless you have a 512GB GPU I guess" to "oh.. I can do a lot of stuff locally now!"

What do you use instead?

To be honest, I never give benchmarks a look. I just use the models for whatever I need to work on, so I can't really make comparisons that are useful for other people.

The biggest gain you'll get is faster memory, provided you have enough capacity to load all the weight into vram. The DGX sparks and Apple silicon memory bandwidth (and also memory access latency) drag down the decode speed quite a bit.

I have two GPU rigs both with 2x RTX Pro 6000, can get ~250 tk/s decode with deepseek-v4-flash in native mixed precision. For context, in antirez's dwarfstar project he only gets ~20-40 tk/s on the same model @ 2bpw on M5 Max.

The latter is for sure usable if it's your only option, but it's really hard for me to personally go back to speeds like that when I've experienced the former.

(Also worth noting dwarfstar only has experimental support for dspark spec dec, when that lands it will definitely give a big boost at higher acceptance rates)

It runs very quickly on my RTX 5090 fwiw. Whole thing is loading entirely into vRAM with a ~130k context size (the max) fitting as well.

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 verifying against documentation.

- Demand increases, but don't feel like running more hardware? Switch to low bit quants, but have a monitor model swap back to quality if it can tell you're running a benchmark.

Assuming model capability plateaus (I think it will), token providers will be in a race to the bottom to maximize profits at the expense of quality that's very difficult to measure.

LLMs are becoming commoditized, which means the margins are trending to zero. It's a lot less exciting to spend another trillion on a new model if you can barely make any profit. Meta getting out of the game might be the smarter move.

Can I ask, do you feel the pain of the level of abstraction? I haven't tried local in a few months, but last time I tried, I felt like I was directing a coding exercise - whereas with a frontier model, it feels more like directing a product building. "I need this feature", vs "write code to do this in this file".

That’s like saying an apartment building is “profitable” because the rent covers utilities while ignoring the real cost which is the mortgage on the capital cost of the building.

It’s funky math and a good way to quickly go bankrupt.

They make money on each token when you look at the electricity and interconnect fees, but no, I don’t think they’ve turned a profit on their Capex, even a little bit

> The major AI labs are gross profitable when selling access to inference.

Do you have a good source for this?

There are lots of points in a spectrum of choices. DGX Sparks, Strix Halos, and the surviving Mac Studios can easily run these 30B class models, just not as fast. So maybe just the leg, but you can keep the arm and first born.

And super noteworthy is that a 27B model (Qwen 3.6 27B) from this year is a huge improvement over a 120B model (gpt-oss:120b) from last year. The goal posts are moving, but at some point "good enough" is good enough for the kind programming I like to do.

I have a custom A3B proxy that caps its thinking off. It is a known issue with the model that Qwen themselves documented but is almost never addressed in any harnesses. I also patched up a few other known bugs in the proxy. I highly recommend you shim A3B and when it hits 2K thinking tokens inject (paraphrasing) 'Time to wrap it up bud! Get to work' into its thinking stream. It almost always gets to work. If it needs more time to think there is always next turn.

In my experience it is almost never productively thinking past that point, just spinning in circles. I also reinject all of the thinking. And there are a few tells that it is getting stuck. I have an optional mode that takes the last few turns and tool calls and shoots it off to DSV4 with a prompt to basically understand where it is at and inject better thinking and or planning. It almost always gets it over relatively difficult humps, but some of the time I don't want things going remote. It might end up with 10-30 cents of DSV4 calls over a hours and the quality improvement is remarkable.

The other thing is I trick it into thinking a web_search tool is a web search but it really just asks DSV4 the prompt. DSV4 is a cheap filter to help prevent prompt injection lol. You can give it other models but DSV4 is my cheap-mode default.

edit: oh! My final 35B A3B tip -- use an extremely simple harness. Pi is good. Pi's default tools almost exactly match what Qwen says they tested the model with (likely meaning that tool set is also what they trained it with or something similar). So, in my experience bigger harnesses don't have a noticeable improve ment on tasks.

the “Actually… But wait!” style responses are so annoying, even Claude opus struggles with this so I’d be interested if meta has done something to cut down on that while still giving good responses

I’m curious why you don’t just use them like a Meeseeks box, rather than compressing and context stuffing into one. One only checks and categorizes your emails, another one for each category of email or even subcategory, one that only handles calendar additions, a different one to check it and notify you; you can go infinite with it. Hell, I’ll have one instance find a file and read it into the context of a different one because I don’t want a bunch of grep commands mucking up the context of the analysis. The find/read one exists for a few moments, as does the analysis one, and the ‘perform’ one is entirely different. I can run them all in parallel and use a queue if needed.

I’m sure you have reasons for your setup though, so I’m curious how you landed on it.

Think of an LLM as a thesaurus, but for entire trains of thought rather than words. Your initial query yields something pertinent to the task at hand. But let it endlessly recurse and... you end up with something completely useless.

People would do well to acquire at least a modest familiarity with what an LLM actually is. NLP is fascinating. So is entropy.

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').

>It seems like the purpose of humans isn't to keep machines busy. When our phone or laptop is idle, it's fine if it sleeps. And when we do want something, we'd rather not wait.

I'm not sure what the original commenter had in mind, but just because our machines are idle when we aren't using them doesn't mean that, that's how we will use computers in the future.

I think notifications are an example even now of the computer not really being idle when we aren't interacting with it.

The fact that you've been posting this idea into the void for 8 months with no pickup is already your answer

Agreed, the trend in this consumer-accessible range is encouraging.

You personally? Just curious. Context window is also a factor and ram isn’t really cheap. Sparks are assembled units which I like.

Native dflash support on day 1 helps a lot! High quality speculative decoding speeds up a lot of agentic work.

Seems like that's the tradeoff with this model. Close to 27b intelligence while using less vram.

Assuming we can even get the hardware in the first place, it might not even be possible for consumers to buy it at any price if it sells out through "agreements" made years in advance https://news.ycombinator.com/item?id=47045459

I kick myself a couple times a week for not getting the 512GB Mac Studio in February. I was holding out for an M4 or M5 chip...

Bought an M1 64 GB for 2000 euro’s second hand a year ago. That was sweet

Pulled the trigger?

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.

If you're dropping thousands on API tokens, you're going to be slowed down at least 10x trying to do everything on a single MBP.

Don't forget about energy usage, you'll probably never break even vs same model on openrouter.

The models people are spending thousands on require more on the range of 600-800gb memory.

128gb hardly runs deepseek v4 flash which is almost free via api pricing.

Problem is that might go away or get nerfed.

If you don't mind exfiltrating all your IP to the API provider

Haha wow. I’m trying to even imagine the AI landscape in 15 years and I can’t.

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.

"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.

It's the size of a big vm. There's nothing wrong with reserving that much working space for one item.

> A model you can run on a loptop is simply not going to work as well as it's needed for programming

The models you can run on a high-spec laptop today are approximately where frontier models were 12-18mo ago (albeit at a lower tok/s rate). If you scan back through hn comments from that era, you’ll find plenty of people saying “this is powerful enough to massively increase my productivity”.

I’m quite optimistic about the long-term future of local LLMs for privacy and cost control reasons. An LLM running on my own hardware, even if it’s not a laptop but a home server, is one where I don’t need to worry about token limits, token fees, privacy, and “rug-pulling” from the vendor.

In the short term, the big challenge is being able to afford hardware that can run a ~30B model. Last month I got to experiment with LLMs on a NVIDIA RTX 6000 Ada Generation as a visiting researcher during my summer break. I see the power of local LLMs for agentic coding; they’re no Claude, but they are quite useful. I wish I had gotten into local LLMs before hardware has gotten prohibitively expensive and in some cases unavailable; Apple discontinued certain Mac Minis and Mac Studios with high amounts of RAM due to the RAM shortage.

Hopefully high RAM prices don’t become a new normal, though the next year or two doesn’t look good.

I've never done it but would be interested because it cuts out the burden of worrying about costs. Maybe I'm mistaken on energy cost here. There's a constant raincloud that follows me around regarding limits, and it would be nice to shake that.

I've been able to accomplish incredible feats (for myself) since GPT-4, so model intelligence is secondary.

For some companies there might be a need to run them locally. For instance, Apple decided to run LLMs on the phone locally. I guess it depends on how important latency and privacy are. Perhaps Meta is looking at how much interest for those local models is there.

> I don't understand the desire to run own AI models for programming locally.

Privacy. Security. Not bulk uploading your trade secrets and intellectual property to Sam and Dario’s servers.

Yet.

While composing a reply to a comment throwing tons of shade on American AI, I took some time to check out the commenter’s HN profile. Their comment history was about 50% such comments. Their submission history started with an article about how Russia was unfairly blamed for some hacking campaign.

It’s entirely possible that this is not a foreign influence campaign. Perhaps there’s a group here that is simply anti-American as its primary interest, and passionately so to upvote each other.

On the other hand, one should not discount the value of HN as tastemaker and trendsetter. Also, it would be fairly easy to run bots here. I wouldn’t be surprised if HN were a field of combat for many parallel influence campaigns, foreign and domestic.

Good points, I personally believe that if/when China takes the lead, they will immediately stop releasing model weights. It only makes sense as a strategy to counterbalance (current) American labs' monopoly on frontier models.

Holding both those positions would be hypocritical all right, but are you sure it's the same people commenting/voting in both cases? I don't think there's a strong consensus on Hacker News. Even something like the time of day an article is posted might get different engagement depending on who is active in which time zones.

Of the two competing models Meta compare Glimmer to in the post, one is Google's Gemma 4.

At this size open weight model, a Western company was already state of the art, Meta is joining that competition.

And my memory is that Gemma 4 got little criticism or doom/gloom. And no, it isn't Chinese.

The GP is claiming Meta is an awful company for what they have done and how they continue to treat their employees. That’s a perfectly ok opinion to hold, and many seem to agree.

Have DeepSeek, Moonshot, or the other Chinese AI companies done such things that attract moral outrage?

Nobody wants to live in a world where one party dominates due to access to superior AI and the others have to fear it (well, except for a few psycopaths who would gamble on being in control of that party). So the underdog will always be the good guy in this race. It has been framed as a race between countries, so Meta fails to be the underdog because they're in the wrong country. That's all.

I don’t think Meta is bad for releasing open models, but are you really going to ignore all the terrible things they’ve done over the years just because of that?

As for DeepSeek or any other Chinese lab, I’m not aware of any practices that would make me consider them a bad actor. Can you say the same about OpenAI, Meta or Anthropic?

It makes me wonder why this dynamic exists here, and I do wonder at times how much our conversations here are influenced by China in a top-down fashion. I'd prefer to think that HN is pretty organic, but that is probably a naive thought.

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

I think I've always had a pretty healthy amount of cynicism towards China. In recent years my cynicism towards the US has increased significantly. I don't see all of my US peers with cynicism, but I think you're living in an age of grift, corporate capture, and unheard of corruption. I also think there's nuance to both. There are some US and Chinese companies and people that I do respect regardless of what's going on politically. (Meta / Zuck isn't one of them though...)

I live in the 51st state though, so maybe I'm just overreacting...

Apparently Americans haven’t got the memo yet that the world is moving closer to China.

Can you point to a lot of posts lauding the Chinese government based on the release of Chinese open models? Because that would be the equivalent to contrast with the OP.

I suspect it's just the generalized anti-West/anti-American sentiments extended into anything and everything. Anything that makes the US look anything close to good goes against their cause and therefore must be countered and talked down.

But you're not wrong about the bias here. You just don't see many comments talking about it because they get mass flagged/downvoted for obvious reasons.

These sentiments aren’t formed in a vacuum. America is becoming increasingly oligarchic and corrupt with decades of experience of companies profiting off harming people and lying through their teeth and Meta is like one of the worst offenders. They are pissing on every ally they have and once again started a war and both have material impacts on other countries.

Americans seem to take the US’ geeat reputation for granted and don’t realize how it has slipped and what that means. They also take for granted that China BAD is truth when this sentiment basically just sprang out of nowhere when the west realized it was their geopolitical rival. But to the rest of the worlds citizens, China is not starting any wars and is the source of cheap goods and innovation to other countries. EV batteries recently. The US is now directly causing high oil prices with their war and exports their rapacious companies like “prediction markets” which are 90% sports gambling now to the rest of the world. Meanwhile the classic American move to these kinds of comments is to claim that negative sentiment MUST be part of some bot campaign because surely no one could actually dislike the great America??

Those factors are all rightly part of the sentiment.

> 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.

Let me be blunt and let me say: you don't understand why we people support Chinese models.

1. Chinese labs started with open weight models, US labs started with dooms day narrative

2. US VC based companies must become greedy to win and return the money, Chinese companies can make 1/10 of that revenue and still be happy

3. Meta in this case, started nicely with Llama, then switched to closed models, kicked out researchers to build data labeler CEO empire inside Meta. Now opening again, what's next? closing again?

There are comments like the one you're replying to on literally every Chinese model release. This is textbook goomba fallacy, btw.

both countries are authoritarian. both are using this "free" tech to spy on people and control them.

why is america and its people anti-china? take a chill pill and worry about yourselves instead of other people :)

There is a big astroturfing going on social media platforms by the chinese. Did you notice 'day in a life of unmarried 30 yr old lady in china' videos flooding usa social media.

Regular ppl in the west now hold mildly positive views of the ccp and how 'advanced' china is than usa.

Then there are europeans who now are looking for china to give them the technology handout now that relationship with usa has soured.

Deepseek never fucked us over. zuck has. A decades of harm creation run doesn’t get excused by the US flag. Zuck is not on your team and if you can’t see that by now, oh my.

> When an American company does anything? Doom. And. Gloom

Meta, "an American company". Being the main driver of an ethnic cleansing in Myanmar - and just sticking your head in the sand when told about it - is just another day's affairs at the average American Acme Inc.

These are comments on a release by easily the most societally damaging Western tech company there is. They so far easily beat Flock, Palantir, Anduril and so on, as a result of their incomparable scale. You're just ignoring that and pretending any negative comments are because it's an American company rather than Meta. That's much more FUD than any pro-China comments I've seen on HN.

Get off HN Mark, you have ten million pervert glasses to sell.

Sorry this comment is a bit snarky, but yours is indeed a sight to behold.

This model doesn’t look solid at all. It comes months after the Qwen model, and in almost half the benchmarks, it performs worse than that. Plus, the next Qwen 3.8 is going to be announced this week. So, this model is DOA.

> FB at one point had a very badass engineering culture

Perpetually kneecapped by one of the worst management cultures I've ever seen

> It's better than the benchmark scores seem to indicate compared to Qwen 3.6 27B. I'm very excited for 3.8

Is it worth considering if it's only marginally better than Qwen 3.6 though? Qwen 3.8 27B is almost there, and will probably be better suited as drop-in replacement for 3.6. Not even considering there's probably going to be a 3.8-35B-A3B too - which will have even better performance.

FB/Meta have had "don't recruit me" databases for a long time that are very easy to get yourself added to, thankfully.

What is the other company that you would never work for?

Meta doesn't need to be "redeemed". They have two of the most popular social media apps in the world. And theyll prob survive without ever having you work there

Mind telling me roughly what you had on your resume that had meta /fb hounding you for a job? ( Of course so I can avoid having this situation happen to me, naturally)

Thomas Bayes would say that the population of people who hate Facebook is really big and the population of people who are scrupulous about whether or not their comments are specific to the matter at hand is relatively small

Certain topics bring out the hidden Reddit inside.

First time here?

Unfortunately there are a few topics that short circuit some terminally only people. One of them being anything related to meta. Few others recently emerging is Flock or Musk. It's really exhausting since you can't have a discussion relating to anything that may be adjacent to said topics. It's like a black hole.

Heaven forbid people have a moral compass and communicate it

This is par for the course, HN is far worse than reddit on balance, especially involving upvoting/downvoting decorum.

Go vibecode something to auto upvote all downvoted posts, call it "Antiechochamber.HN" or something, and if enough people used it this website might improve a bit.

It's trendy to hate on Meta just like it's trendy to handwave on Apple.

One can do no right regardless, the other can do no wrong.

At least in HN.

The latter bankrolled and continues to bankroll the former. It is not incorrect to conflate them.

All models come with some bias. Given Meta's track record, I would not touch anything from them with a 10 feet pole.

Social media, often owned and perpetuated by Meta, has poisoned the world. It is not redeemable at this point.

I think it’s also worth pointing out that that there are numerous less evil options to choose from.

Perhaps none of the AI companies are shining examples of high ethics, but basically all of them have ethical high ground over Meta.

At least Anthropic isn’t sending private videos from pervert glasses to contract workers in Africa. It’s a low bar but it’s a bar nonetheless.

It is very bad with any svgs.

Picasso's Pelican

Just tested muse-glimmer:30b-mlx on my laptop. Works great although a bit slow.

Is the assumption here that inference costs will stay roughly static, or that frontier models will keep getting more expensive quickly enough to offset efficiency gains?

Because I don’t think “the subsidization train is going to end” necessarily means current pricing becomes impossible.

If capital keeps pouring into frontier AI, companies still have an incentive to subsidize access while competing for users and market share. And if that subsidization starts drying up, there’s even more incentive to bring inference costs down by making smaller and cheaper models catch up to today’s frontier capabilities.

So either way, I’m not sure you can extrapolate from the cost of serving current frontier models to what equivalent capability will cost two years from now.

Small models are still great for lots of “simple intelligence” use cases, like annotating or summarising files and media; or even just basic chat when given web search tools.

My local NAS is private and I’m not going to send it off to APIs for captioning or metadata; but even Qwen3VL 8B does an excellent job at this, despite being quite old.

They are also really excellent for fine tuning. Unsloth and Tinker (from Mira’s TML) are great places to start.

If your use case is narrower than “coding agent for everything”, you can probably match frontier performances on that narrow domain with ~30b and exceed it with ~100b+.

some small models are fast, and fine tuning can be done locally

for example in gaming context, if you need an answer below 5 seconds, they are the sweet spot

Gemma4-E4B (4B params) works pretty well as a local wiki, or when you don't have connectivity.

2a03:2880: by any chance?:

https://news.ycombinator.com/item?id=48137854

Have asked them to stop numerous times and they just keep hitting for about eight months now.

Right, so it's as correct as me claiming it to be an E-Mail sorting model. It may be good at that, but that's not its primary purpose.

Don’t sleep on NVIDIA and Nemotron.

It’s not completely open source, but they actually release their pretraining and post-training datasets with some redactions for (cough) pirated content.

They also have very good code and playbooks for actually doing a fine-tune, CPT, etc.

Even if you’re not tuning a Nemotron model, its mixes are very excellent for your replay data slice; or general experiments. Way better curation and quality than Dolma, etc; or other large huggingface data mixes I tested.

There was a good discussion yesterday on the DeepSeek Flash release thread about this.

There's a large market, very large, who want the best regardless of what it costs. Probably a large enough market to keep that domain of research afloat (as opposed to shifting research manpower to cost cutting).

The reasoning is just that the marginal cost of AI is very secondary to fixed costs of the businesses themselves; it's not an excuse to sacrifice performance.

GDM -- Ok, I'll bite. Why are you including them?

Dense models run at a very usable speed (Qwen 3.6 was running at ~50t/s last I looked) on my dual 7900 XTX desktop. (And before anyone brings it up, I did not buy them for this purpose, so the up-front cost is irrelevant in my case.)

3090/4090 probably would do 40 t/s, for 5090 75 t/s is shown in the blog.

The benchmark comparison is against the dense variants not MoE

I assume due to price. The 5070 Ti costs ~$1k, the 5090 costs ~$3.5k.

why don't you use the quantized version of kimi-k3

Thanks. Ah yes, I skipped over their own figures in the article.

K-Quant-17GB seems possible, though as they state 24GB.

You have to look a little past the current hysteria.

Any pointers to more info about that? Sounds interesting.

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.