Qwen 3.8-Flash-Next releasing tomorrow (125B a6B)
Comments
SwellJoe
sosodev
That’s only true if you think AI is the only reason to own a powerful and efficient server. Mine does plenty of traditional server stuff too.
SwellJoe
I can do traditional server stuff on any old computer with a big hard disk and a decent amount of RAM. That's not worth $3500-$4000. When RAMpocalypse is over and we can buy a Strix Halo for under $2000 again, the math starts mathing. It becomes a pretty great desktop computer that also happens to run AI pretty well at a pretty good price.
sosodev
Yeah, but that computer can’t also do the AI stuff. And not everybody has a desktop with multiple 32GB GPUs available.
I’ll admit though I’m biased because I bought my board for $1600 back before the prices went crazy.
downrightmike
Oh no a tough constraint that will lead to further innovation like deepseek. How terrible.
ArvidSu
An "AI" server can do traditional server stuff but a traditional server can't do AI stuff (inference)
jubilanti
A 3060ti 8gb, released in 2020, has 448 GB/s of bandwidth compared to the Halo 256 GB/s
The 3080ti is 912.4 GB/s
embedding-shape
And the newly announced/launched Apple M6 has 170GB/s of unified memory bandwidth, meanwhile M5 Ultra gets 1.2TB/s of unified memory bandwidth. https://www.apple.com/newsroom/2026/08/apple-introduces-m6-a... Not sure if the first one is a typo on their press release, can't be just 170GB/s then be pushed for AI use, can it? Could be a different measurement I suppose...
yjftsjthsd-h
But it also has 8GB of RAM.
SwellJoe
Enjoy Gemma 4 E2B at blistering speeds, I guess?
throwaw12
how much performance (tok/s) can you expect from 128GB Strix Halo? assuming this model will be released with FP8
also can you use it for fine tuning?
SwellJoe
The Strix Halo and DGX Spark are pretty danged slow, relatively speaking. I don't recall exact numbers, but with MoE models in this size ballpark (Laguna S 2.1), I seem to recall I was seeing about 20-25 t/s with a big context, which is close to usable. Qwen 3.8 27B crawls on this hardware, though, at 10-16 t/s, definitely not comfortable for interactive use. (Though this makes it seem like you can cook pretty good with a 4-bit ROCmFP4 quantization: https://github.com/julianmb/q38rocm the model does get notably dumber below six bits.)
A model similar in size to Laguna S 2.1, but with only 6B active parameters, should be a notable amount faster, so I would imagine 25-30 t/s would be a reasonable guess for where Qwen 3.8 Flash Next will land.
DFlash2 might improve all these numbers. It wasn't available last I was testing new models on the Strix Halo; I've only used MTP (which doesn't generally improve MoE models, but I believe DFlash2 can).
Given software improvements, I'm hopeful an MoE in this size range will be the sweet spot that pushes past 40 t/s and is also smart enough for real work. Qwen 3.8 27B is finally a self-hostable model that's smart enough, but it thinks so hard it still isn't really useful for agentic interactive use.
Note also prefill with large models is pretty slow on the Strix Halo (300 t/s, maybe). Time to first token is a painful wait, when using it interactively with large models.
downrightmike
You can only use up to 90gb for the GPU, so it doesn't fit
SwellJoe
No. In Linux, you can ignore the BIOS (well, set it to 512MB) and set GTT to allow almost the entirety of memory for GPU. If you don't run a desktop GUI (and why would you), you don't need memory for much of anything else. Just a few GB for the OS and llama.cpp.
These are roughly the settings I use: https://github.com/kyuz0/amd-strix-halo-toolboxes#kernel-par...
ddtaylor
I enjoy the Qwen models a lot, but building things on top of them with OpenRouter has been painful.
OpenRouter does a lot of great work and I really enjoy being able to use different models so easily. I like when a provider is phasing out an older model that still works for my needs and the price is much lower. It seems like such a good win-win.
However, the problem is that many Qwen models have almost no capacity or is so flaky you literally have to just litter your code with a blacklist/whitelist of providers. OpenRouter has some attempts to solve this, but they don't work. In fact, OpenRouter has a lot of really cool stuff that is documented, but if you read the code it's not yet implemented or isn't actually there yet, which is a shame.
I tried to get in contact with them at OpenRouter about this and I was interested in working with them in the past, but it's difficult to get in touch with the right people and they are growing very fast. I expect being acquired by Stripe will accelerate those problems in some ways. I have no doubt they will resolve all of these issues eventually and scaling that much that quickly is really hard, so kudos to them, but the road has been pretty lame and taken some wind out of my sails.
irthomasthomas
Openrouter was pretty great before prompt caching became common. Now it is extremely expensive for most individual workflows, unless you spend a lot of work customizing router preferences, and then you still get a worse cache hit rate than using the provider directly. I only keep $5-$10 in OR for occasional testing.
dackdel
didnt stripe acquire open router? so i assumed its sunset.
runeblaze
genuine question — how has fireworks or baseten or $reputable_inference_provider worked for your use cases? most production workload probably works fine with one of these and another set as fallback, at least so i think
geek_at
The best solution to this for me is to self host litellm or a different router and use model aliases. For example I have a model called "coding" and when a new good model comes out I just switch the backend without needing to change the alias or the key in my projects (opencode, etc).
I have a few of them even a smart router called "agents" which will use local models but if it thinks the request might require higher reasoning it's routing to a different model
try-working
I built a router that lets you route between local and cloud models. Link in my profile.
embedding-shape
Yeah, I also built my own "router" for this:
if (process.env.LOCAL_MODEL {
http('localhost:3000/v1/completions')...
} else {
http('api.openrouter.ai/v1/completions')...
}nvme0n1p1
Brilliant! I'd like to invest $10M. Who should I make the check out to?
npn
I'm confused? Can you just define some presets and call them instead? With preset you can pinpoint a lot of things, especially the providers
notnullorvoid
It will be interesting to see the intersection of this with inference engines like FreeToken which improve distribution of work for MoE models across CPU/RAM and GPU/VRAM.
If all it takes for a competitive model to run locally at good speeds is a used 3090 and some DDR4, then we might be in for the year of local AI.
kamranjon
Have you tried FreeToken yourself? I was hoping to find some benchmarks on their github but took a quick pass at their research paper and it seems they're showing ~2x performance on qwen 3.6 35b when compared to llama.cpp - but llama.cpp is so sprawling and has so many options I find that a difficult comparison.
notnullorvoid
I haven't yet, though plan to when this model is released. The models that I've been daily driving (Gemma 4 26B, Qwen 3.8 27B) have fit nicely on my 3090. I think FreeToken only offers a perf increase for MoE models that you can't feasibly fit in VRAM.
Yeah I'm sometimes unsure how to get best perf out of llama.cpp, and honestly thought it already did what the FreeToken paper discusses, but from everything I've been able to find since llama.cpp has no dynamic expert cache for GPU. An RFC discusses adding such capability and there's impressive results some are claiming from a fork, but I had to stop reading the thread, reading all the LLM generated comments and summaries from people was making me dizzy.
RFC here https://github.com/ggml-org/llama.cpp/discussions/24528 which also links to some experimental implementations throughout the thread.
Zylokloto
You can already run it locally its just not the same.
It is still slow, a lot slower than what you are used to with claude and co.
And as soon as you increase context size, your memory requirements jump.
Then when it runs for 30 minutes for something claude needs 5, your device will get hot.
And even a used 3090 is apparently now between 1-2k.
notnullorvoid
> It is still slow, a lot slower than what you are used to with claude and co.
That really depends on the model, I run a few models locally. All at speeds comparable to or faster than Opus.
In general we haven't reached the ceiling for what performance we can get out of consumer hardware. As evidence by FreeToken which hasn't even added MTP/speculative drafting support yet, which will add another boost.
> Then when it runs for 30 minutes for something claude needs 5, your device will get hot.
I doubt the timing differential here, but even still I run my 3090 pretty heavily with inference workloads and it stays cooler than when I use it for gaming.
> And even a used 3090 is apparently now between 1-2k.
Yeah I guess the price went up significantly in the last couple months, used to be hovering around 1k. 3090 isn't the only option though.
ekidd
> That really depends on the model, I run a few models locally. All at speeds comparable to or faster than Opus.
Yes, a lot of Qwen3.8 27B setups are actually quite snappy, as long as they fit 100% in VRAM. In my testing, I wouldn't go below 32GB of VRAM, though—you really want a 6-bit quant and 8-bit K/V quants minimum. I've seen too much weirdness out of 4-bit quants since Qwen3.8 shipped. I think it may be damaged more than 3.6 at similar levels of quantization?
If hyperscalers hadn't bought up almost all the fast RAM production for the next several years, 32GB of VRAM would be tolerably cheap—a lot by "home PC" standards, but not terrible by "professional tools" standards. Sadly, the RAM market is amazingly ugly right now.
> I doubt the timing differential here, but even still I run my 3090 pretty heavily with inference workloads and it stays cooler than when I use it for gaming.
Yeah, running inference on a laptop is likely to run quite hot. But in an ATX case with decent cooling, it's generally a lower load than gaming. One handy tip: Many Nvidia GPUs (and some from other manufacturers) support power limits. For example, limit a 5090 to 400W instead of 600W, and it will run much cooler. You might lose 11% off your tokens/sec (depending on the exact card).
nitin7
Which models when run locally come close to Sol and Opus, from your experience? And which harness do you use?
blahblaher
yeah, but otoh... f* Anthropic and OpenAI
vegnus
I have an m1 max 64gb macbook. Anything I can do to get 3.8 27b at more than 10 tok/s or am I relegated? 3.6 a3b is good but its not as good
syntaxing
Really looking forward to this, 27B is a struggle with a strix halo and Laguna 2.1 can do stupid things for tooling calls.
cpburns2009
Yeah 27B is way too slow for the Strix Halo. Laguna was better but still slow when I tried it. Qwen3.6 35B is still the best today.
SparkyMcUnicorn
Have you given Ornith-1.5-35B a shot?
It's been a pretty decent step up for me compared to Qwen3.6
cpburns2009
I know it benchmarks very well. I haven't tried it yet though.
puzzlingcaptcha
What sort of pp/tg speed do you get on a Strix Halo?
cpburns2009
This is the best I got, all with Unsloth's quantizations.
Laguna-S-2.1:UD-Q4_K_XL (no MTP) pp=186.4 t/s tg=27.8 t/s
Qwen3.6-35B:UD-Q4_K_XL (with MTP) pp=404.4 t/s tg=83.2 t/s
Qwen3.6-27B:UD-Q4_K_XL (recorded pre-MTP) pp=343 t/s tg=12.1 t/s
Laguna actually performed better than I remembered. I thought it was slower.
ascii0eks84
What are pp/tg? I get 30t/s on 27B qwen.
throwawayffffas
pp is prompt processing how fast it processes the prompt. Tg is token generation how fast, it generates tokens.
cyanydeez
it'll hopefully improve with more MoE and half the prefill/generation. I think it's the sweet spot for the strix halo for smarter or vibe tasks.
corysama
So, I know https://cactuscompute.com/needle is designed only to enable tool calling on tiny devices. But, I wonder if anyone has used it as a CPU-side mediator between a tool and a GPU-side local LLM making semi-natural-language tool requests...
big-chungus4
> We are releasing these architectural improvements ahead of time so that the community can prepare for the upcoming full family of Qwen4 models.
That gives me hope that "full family" means it will include smaller models like 4B.
c16
+1 to the long list of people hoping for Qwen3.8-27b A3B.
NitpickLawyer
They've said no moe for 3.8, and since they're already releasing a qwen4 early preview, they're probably focusing on that arch going forward.
kamranjon
Where did they say that? My understanding of this 3.8-Flash-Next release is that it's a MOE (as per the title of the posting here, 125B a6b)
WiSaGaN
You probably meant Qwen3.8-35B-A3B. But judging from some of the words from their team, it seems unlikely unfortunately.
vorticalbox
They normally release a 35b dense and an 27b moe (4B active per token)
For context 35B on my m4 runs at 10 tokens a second, 27B moe runs 50-60 tokens a second.
pwython
I was already rolling around the idea of a 128GB M5 Max MBP. Now this!
A 4-bit MLX quant with 128k window should fit perfectly, in the 50-70 tok/s range.
Eric_WVGG
Just out of curiosity, why run "local-local" when you could just set up a Mini or Studio at home and query it over http? [edit] whole conversation about this in another thread https://news.ycombinator.com/item?id=49433413
I’m personally considering retiring my MBP for a Studio + 15" Air whenever this MBP ages out.
kamranjon
I actually do this with my MBP - it's a LLM server when I'm working - and then when I'm not it's just a really great machine for video editing and other media work.
LeBit
This is the way.
I’m doing that. Mac Mini M4 Pro with 48G RAM as a headless llama.cpp server.
I much prefer using " thin clients " as the interface to the big VMs running in my homelab
sscaryterry
I have a 128GB M5 Max, and it sucks at this stage. 50-70 tok/s might be something...
smcleod
50-70tk/s is what I get on my m5 max on a 5-6bit Qwen 3.8 27B?
Casteil
I don't know what black magic you're up to but I see more like 30-35t/s on a 16" M5 Max using 3.8:27b Q4, regardless of whether it's mlx or gguf.
qwen3.5:122b-a10b is significantly faster at around 60-65.
syntaxing
With MTP? I get 25-30 TPS on a strix halo. 50+ on a M5 max should very doable. Dflash (2) will push your TG even further
sscaryterry
I tried 8-bit, perhaps I should try 6-bit.
irthomasthomas
IDK, prefill speed is a bigger concern for most wokflows, like agent coding, and I heard that this is quite low on macs?
smcleod
That was mainly before the M4 generation when they didn't have matmul instructions.
jasonjmcghee
M5 prefill is much faster than M4.
I've seen benchmarks that show 4-5x faster of M5 Max vs. M4 Max.
For local models you're likely using M5 Max, prefill is low thousands of tokens per second, as opposed to, say high hundreds with M4 Max.
For larger dense models, some fraction of that, but similar multiple.
smcleod
Yes, I have the M5 Max. But there was no matmul acceleration before the M4 which made things a lot slower.
Catloafdev
Very curious to see how this compares to Deepseek v4 Flash. I have to assume they wouldn't be releasing this if it was worse.
natrys
Why not? It's not really competing in the same size class.
Besides, as they explicitly wrote here, the main goal for this release is not performance, rather to serve as a reference for inference runtimes about what to implement. So that later Qwen 4 can be released with zero day support.
Catloafdev
Good point, I didn't see that. I guess I categorized them in the same bucket of 'runs on 128gb machines'
Guess Qwen 4 is the one to wait for.
NitpickLawyer
Their "next" variants are usually undercooked, but useful for the community to verify support for inference stacks. This will likely be the same.
hedora
Time to dust off my 128GB strix halo (literally—it’s been dusty, and it’s running a bit warm these days).
Any idea where this model sits according toquality benchmarks? Pre-bubble MSRP on this hardware was $1400, and it draws 200-ish watts, putting it down into consumer territory.
I’m wondering if it can replace claude for llm-friendly coding tasks.
cpburns2009
So back in the Qwen 3.5 release, the 122B-A10B model scored slightly better than the 27B model. I'd expect this new 125B-A6B to perform similarly to the recently released 27B. Qwen3.8 27B is supposed to rival Sonnet/Opus 4.6.
hugmynutus
Qwen3.8/Qwen3.6 has a weird self doubt/thinking too much problem. You can prompt it away. I would say it "approximates" Opus 4.X class models well enough especially for coding/linux problems.
The only reason I stopped using it as much is I was getting 25-35tok/s on Intel B70 (non-quant) which made some responses slow. For a long running/autonomous task, it would probably be sufficient.
wongarsu
There is the rule of thumb that if you take the geometric mean of the total and active parameters of an MoE model you get the equivalent size of an equally capable dense model.
If you follow that formula, you would expect a 125b-a6b model to match a 27b model (sqrt(125*6) = 27.3). That does not feel like a coincidence
hedora
Thanks. My current stack ranking of anthropic models is:
4.6 ~= 4.8
4.7 much worse.
Fable and newer consistently tells me to pound sand, so I’m not sure what I’m paying $200/month for. 4.8 sometimes does too, but it’s at least usable most of the time.
So, I’d expect this to mostly replace Claude for my workflows. The main tradeoff for me should mostly be token throughput vs. no longer really trusting anthropic.
cyanydeez
I've got the A10B hooked up to deer-flow and it does remarkable well when you dont need to baby sit it.
_ache_
What will be the requirement, like 128G of RAM and 12G of VRAM ?
embedding-shape
How long is a rope? Technically you could probably run it off a SSD, but it'll be slow as molasses. If you want it "fast", you want it all within GPU and VRAM, who knows what that'd be. If the engram parameters are separate, I guess it'd be like BF16 ~400 GB, FP8 ~200 GB, NVFP4 ~100GB. Otherwise maybe like ~300GB, ~150GB and ~70GB or alike, don't quote me that, only some guesses. The one who waits will see :)
honestlyranked
Alibaba is giving sleepless nights to the tech giants
drannex
To be fair, Alibaba IS a tech giant, one of the biggest in fact.
They are just giving sleepless nights to the western tech giants.
WithinReason
Sounds like a line from a fairy tale
big-chungus4
I hope there is going to be a free endpoint... Unlike 35B-A3B, I am nowhere close to running it locally
fkndkfn
I can feel Dario Amodei's tears in the announcement :)
system2
These companies are naming their products worse than I was naming my half-baked software in the 90s as a junior developer.
bellowsgulch
Really happy for those with 128GB+ RAM. Sitting here with my Apple M1 Max with 64GB though. Was looking forward to a Qwen3.8-35B-A3B like many others.
dofm
Have you tested Muse Glimmer in low reasoning strength?
Token generation is slow (and prefill is) but you will likely find it solves actual problems faster than Qwen 3.6 35B-A3B.
bellowsgulch
I’ll give it a try! Thanks for the heads up!
dofm
I’m using the Unsloth 4-bit quant.
To change the reasoning strength you just put text in the system prompt.
From memory it is:
Reasoning strength: lowtarruda
Can you share the source for the parameter count (125B A6B)? I didn't see it anywhere in the page.
petu
It was in description under the countdown initially, but was quickly removed.
It also said 51B of n-grams and new attention (IIRC it said "Qwen Sparse Attention").
edit: here's a random screenshot https://x.com/AiBattle_/status/2092210011858460819/photo/1
NitpickLawyer
This is what I copied from the en version of the modelscope page, right when they published it:
> Redisgned Multimodal MoE Model: 125B main model parameters, supplemented by an additional 51B N-gram embeddings,and 6B parameters activated per token.
> Efficient Training and Inference: Significantly reduces training and inference costs. At ~1/9th the training cost，Qwen3.8-Flash-Next achieves comparable capability against Qwen3.7-Plus, while being more capable in areas of coding and cowork.
There was another paragraph about a new attention, but I didn't copy that.
lousken
gpt oss killer? this can easily run on a server cpu with its memory bandwidth
Art9681
You must have hibernated for a year. Most modern 27b models can outperform gpt-oss-120b.
bearjaws
Not exactly surprising given it's a dense model at 2.7x the size of the experts in gpt-oss
stymaar
gpt-oss is long dead though. It's been 8 month since Qwen 3.5 was released.
dmead
This is great. I have a weird system layout (192gb system ram, 8gb vram). the mixture of experts models have been nice when i can run the dense reasoning layers on the gpu (which somehow fit?!) and then the expert on the cpu.
its worked out to to 40 tokens/seconds on their 80b-a3b model. we'll see how much of a hit this is.
freddiehdxd
Does it support vision?
cogman10
Wow. I wasn't expecting this. I thought they were going to do a 35B model instead.
hasteg
As a 5090 owner and local model enthusiast, I was hoping it would be 35B A3B so I could run it myself =(.
Tuna-Fish
The 27B one is great on a 5090.
This one is basically aimed at macs, Strix halo and DGX Spark.
cpburns2009
You can run the 27B released last week. I haven't tried it yet myself but the 3.6 version runs great on my 5090.
Philpax
Strongly recommend https://github.com/Neroued/ninfer, which can pull ~180 TPS on 5090 with 3.8, and 500 (!) with 3.6 35B-A3B.
cpburns2009
I've been waiting for the dust to settle on this model so I can find a good runtime setup. I'm definitely bookmarking this. Thanks!
BrucecarlL
Waiting for the performance report! Ai hope it can beat DS
isatty
Can I run a fp8 quant with 96gb VRAM?
cpburns2009
Only VRAM? Unlikely unless you can also load the whole model into regular RAM. The previous 3.5 release was 250gb at BF16, so FP8 would likely be around 125gb. Your best best is FP4/Q4.
isatty
Very sad. I try not to go below q8.
Alien1Being
AI VENDOR PRESS RELEASE
tw1984
Qwen4 sounds exciting
luciandan
Coming soon...
cyanydeez
oooh, I like a6b; that will be nice. 3.5 A10B qwen works really well in deer-flow when you want to seriously vibe code or research and you're just not going to baby sit.
onesandofgrain
where are the humans geez
blurbleblurble
gg
david927
Well put and succinctly put. And if OxA is a flash model? it becomes: goodnight
mrdoe
lol blocked with dns4eu
what a joke this resolver has become
Finally, a reason to own a 128GB Strix Halo or GB10 device. Or a reason to consider the new Mac Studio.
I have a Strix Halo and dual 32GB GPUs in my desktop, and the latter is pretty much always better for running local models because it's quite a bit faster due to higher memory bandwidth. There simply haven't been any models that are better than Qwen 27B or Gemma 31B, which run comfortably in 64GB with big context.
And, MoE should make it run at a close to usable speed.