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.
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.
> 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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Really looking forward to this, 27B is a struggle with a strix halo and Laguna 2.1 can do stupid things for tooling calls.
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.
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.
https://github.com/FlashML-org/FreeToken
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.
> 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.
yeah, but otoh... f* Anthropic and OpenAI
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.
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.
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
I built a router that lets you route between local and cloud models. Link in my profile.
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
> 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.
HF link: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
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.
I have a 128GB M5 Max, and it sucks at this stage. 50-70 tok/s might be something...
50-70tk/s is what I get on my m5 max on a 5-6bit Qwen 3.8 27B?
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.
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
I tried 8-bit, perhaps I should try 6-bit.
IDK, prefill speed is a bigger concern for most wokflows, like agent coding, and I heard that this is quite low on macs?
That was mainly before the M4 generation when they didn't have matmul instructions.
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.
Yes, I have the M5 Max. But there was no matmul acceleration before the M4 which made things a lot slower.
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.
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.
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.
Alibaba is giving sleepless nights to the tech giants
Sounds like a line from a fairy tale
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.
I hope there is going to be a free endpoint... Unlike 35B-A3B, I am nowhere close to running it locally
Can I run a fp8 quant with 96gb VRAM?
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.
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.
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.
I’ll give it a try! Thanks for the heads up!
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:
Wow. I wasn't expecting this. I thought they were going to do a 35B model instead.
As a 5090 owner and local model enthusiast, I was hoping it would be 35B A3B so I could run it myself =(.
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.
Strongly recommend https://github.com/Neroued/ninfer, which can pull ~180 TPS on 5090 with 3.8, and 500 (!) with 3.6 35B-A3B.
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!
The 27B one is great on a 5090.
This one is basically aimed at macs, Strix halo and DGX Spark.
Waiting for the performance report! Ai hope it can beat DS
where are the humans geez
Can you share the source for the parameter count (125B A6B)? I didn't see it anywhere in the page.
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
Qwen4 sounds exciting
gg
Well put and succinctly put. And if OxA is a flash model? it becomes: goodnight
lol blocked with dns4eu
what a joke this resolver has become