README could clearly make use of a cleanup, seems to be more like a session log dump now than a good introduction to the project for a new user. Maybe try something like "Remove anything from the README.md that wouldn't be helpful to someone who sees this project with zero context, for the first time. Rewrite all paragraphs and sections to be concise and remove all fluff, leave only important details new users must know before using the project".
I hate this AI style writing because since it doesn’t really understand flow, it’s being inserted in irrelevant places and it is extremely irritating to read.
For what it’s worth, this comment was not targeted at you, but rather the model kinda forcing it. I get the sense that Anthropic did not think much of this, but it seems to have gotten worse with recent models and it really comes off as a kind of nails on the chalkboard writing style.
I have to image whatever style of writing this was trained on is a lot more pleasant to read and I feel bad for whoever writes like this now being associated as bad AI writing.
most of this stuff isn't really possible at this point _without_ models, so pissing and moaning about people publishing work and not humanizing is really silly.
I used similar prompts before. Now I simply say to "remove historical cruft" and results are good enough. It's the model itself that first used this wording, I found it concise.
I have a 48GB M5. I don't need to run larger models. I want more context. I've managed to set the context window at 71,680 using Qwen3.8-27B-oQ4e-fp16-mtp. But I want more. Is anybody, with similar specs, able to set their context window higher?
We are running 35b-A3b with 264k context (the model's default max) using vllm and the "frog" jinja templates: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates and had good luck. We are mostly running agentic workloads though, rather than coding. 27b has a slightly higher agentic job completion rate (95% vs 92%) but the 3% trade off is worth it because the A3B is sooooo much faster, and we reprocess the other jobs with a different model. Don't sleep on the froggeric templates.
Qwen: Looking at you for a new ~35B MoE! Please and thank you
I am running 3.8 27b at q6 quant with 160k context on a 32gb video card (arc b70 pro) - I quantized the kv cache at q8 - that is the only trick really - works great.
It's hard to believe 16GB unified memory will give you 5 tok/sec unless you are ignoring the thermal warnings. I am running Qwen3.6-35B-A3B on my 16GB M3 and get 7-8 tokens/sec with all the optimizations while keeping the peak memory and thermal warnings at check. https://github.com/deepanwadhwa/samosa-chat
Now I'm feeling pretty good about getting 10-11 tokens/sec running Qwopus 3.6-35B-A3B Q6_K on an old Mac Pro 2013 (trashcan) with 128GB RAM (DDR3), 12 core Xeon, dual D700s. Arch Linux and llama.cpp.
Not a mac/UMA discussion point, but is it time to add additional, installable, DDR5 to GPUs? I can see this as a win/loose. PCIe 5x16 is close to maxing out the bandwidth available from high end dual channel DDR5 now, but not quite. I'm not a hardware person but I suspect putting it on the card could lead to significant performance improvements over using system ram so allowing systems like this, where MOE weights are shed, to get even higher performance than just adding that DDR5 to the system. Bigger models become closer to reality and it provides more of a pathway for developing technologies that take advantage of it. Of course the loose side is that you just put a lot of specialized ram on a card instead of into the system where it could be used for other things. I could see a place for a 16GB card with 64GB(or more) of DDR5 especially if we start seeing MOE and similar technologies really start being designed for this concept.
Yes, but also 12 tok/s versus Claude is so far from comparable. I know that it’s not exactly 1:1, but it’s a long way from an easy trade-off, especially considering hardware prices for high levels of RAM.
yes! I'm bullish on this. there is a lot of work to do. I've been experimenting with pruning, distillation, and retraining too. I'm sure your 32gb m6 will run a badass local model!
As someone who is just looking at the theoretical benchmarks of each of these models I'm curious if anyone could share what are the problems (maybe around code) that flash-next was able to solve which 27b was not able to
I love these efforts to get proper models running on lower cost hardware and I think this is where the next real breakthrough will come from. The more efficient this sort of thing can be done the bigger the chance to democratize this tech, 'good enough' is what you need and as long 'top of the line' gives a competitive edge even if it is at a cost there is a substantial risk of the door closing on general computing at some point in the near future. Keep in mind that there is no guarantee that the pendulum has to swing back, it can swing one way and get stuck, and then you're going to have to beg for crumbs from the haves.
I think there's a very good chance that history will rhyme a bit.
DOS/Windows and PC clones were by no means the best available, but they were cheap, ubiquitous, and versatile compared to alternatives that were either much better at one task but more expensive or better at everything but wildly expensive. They were "good enough" and represented a solid improvement over what many existing computer users had as well as a good entry point for new users. As such they spread like wildfire and became the standard while the expensive alternatives either became hardcore niche or vanished.
Both projects are different in scope. Think of slotstream as optimizing for memory and for this specific model for now, my intention is not to build an inference engine the same as oMLX
32GB dedicated to an N-gram table instead of a draft model is an unusual choice for speculative decoding — what made it win over the more common draft-model approach here?
Using macos on low memory regimes will make it use disk-based swap.
For example, a Macbook Neo (so in theory, something with around 4GiB of free RAM lying around) might eat around 900GB of writes a day while not doing much at all, because it's basically on low on RAM and swapping all the time.
Reading causes insignificant wear ("read disturb") that likely isn't a problem, but I don't think it's possible to issue pure reads to modern SSDs. The NVMe spec mandates tracking the amount of data read, and this has to be written to the drive. I'd hope the firmware buffers this and writes it at low frequency, but on the other hand, I doubt the firmware was tested in extreme random-read regimes. Unexpected failures from excessive statistics recording could be possible.
There are already a handful of repos doing essentially exactly this: `mlx-moe-offload`, `streamlx`, `mlx-moe`, `mlx-flash`, and `deepseek-v4-flash-mlx` - i.e. keep the resident parts of an MoE in unified memory and page/stream routed experts from SSD on Apple Silicon.
At this point I'd much rather see people collaborate on one of these implementations, benchmark against them, or upstream the useful bits into MLX/MLX-LM instead of producing yet another near-identical repo.
The local-LLM ecosystem really does not need every implementation idea rediscovered five times and wrapped in a new README. AI-assisted coding makes producing a new repo cheap; maintaining, benchmarking, and integrating one is the actually valuable part.
I see your point. As an oss defender myself, I agree, however, the spirit of this is to see how fast I can make it. I'm sharing this with the community, which I think is aligned with the original oss spirit.
It's an experiment for myself but I am committing to maintain it. I've been an oss person for a loooong time, way before AI was a thing. Think about it as a new, from-scratch take at it, not as a re-reproduction.
Hey carloslfu, kudos from the other side of the internet, don't get down on people nitpicking everything here, experimenting and discovering is part of learning so keep going!, remember this is the place that said dropbox was dumb and could be replaced by a script.
I think multiple people working on the same thing is great.
Everyone comes at it from a different point of view, and some approaches work, some don't. And when people do this themselves they learn. Existing projects have their mistakes worked out already.
Maybe one of these people is going to come up with the thing that nobody else thought of because of their experience working the problem from scratch. You may not get that from someone working from an existing project, because existing projects have their approach "baked in."
What all these projects are showing so far is that it's possible to stream from disk, but that the performance isn't ideal. But I'm sure you could take this approach with smaller models and get better performance.
In addition, it's a given that when you work with large data sets performance means organizing the data to take advantage of caches, both disk and cpu. It's not clear how that would work, exactly, given that each run is a not-quite-random walk through the data. The Big Data way is to prebuild all of that as much as possible, which is probably impossible with a big model. But what about a smaller model?
This is one of the aspects of this year that I've been finding very grating and wasteful. Collaboration still happens among people with the ability to do so and the technical skills, but everyone else is taking their own helicopter to the top of the mountain, "putting it out there", and there's just a ton of redundant projects that do the same thing.
I'm only joking anyway — it's more a comment on the whole AI-accelerated trend of everyone having their own version of a thing.
I do agree that, ultimately, combining your efforts with others working in this whole area is probably really worth it, but I can see how there's an ease of pushing forward on your own these days.
I do not have fast internet so I am not sure when I'll really be able to download the weights but I do have an M1 Max to try this on, so I will at some point!
I see! yes, downloading the weights part is painful. I tried a couple fixes and it is as fast as it can get downloading from HuggingFace. I think the field is heading toward smaller, more capable models soon, so you won't have to wait that long!
I mostly use Muse Glimmer, which runs quite well on my M1 Max as it is 30B but it also reasons very efficiently. I have tried the Qwen 3.8 27B which is also usable if quite slow to reason, but I guess like everyone the idea of the Flash-Next model holds some intrigue, because the 35B-A3B is pretty good.
I'm sorry this makes it seem like I didn't do my research. I did a TON. To fix it I'll add a benchmark/comparison table. Also, I wouldn't call it market research since this is not commercial AT ALL.
> Hugging Face is the bottleneck, not your link.
README could clearly make use of a cleanup, seems to be more like a session log dump now than a good introduction to the project for a new user. Maybe try something like "Remove anything from the README.md that wouldn't be helpful to someone who sees this project with zero context, for the first time. Rewrite all paragraphs and sections to be concise and remove all fluff, leave only important details new users must know before using the project".
I don’t want to be a cranky codger, but I dont get why 5 minutes of work cleaning up the README can’t be done before posting to HN.
> Run Qwen3.8-Flash-Next on a Mac that can't hold it
This is the first line of the README. I can't believe people are becoming ok with this, and I'm 100% on the AI train.
I hate this AI style writing because since it doesn’t really understand flow, it’s being inserted in irrelevant places and it is extremely irritating to read.
I feel you! fix incomming
For what it’s worth, this comment was not targeted at you, but rather the model kinda forcing it. I get the sense that Anthropic did not think much of this, but it seems to have gotten worse with recent models and it really comes off as a kind of nails on the chalkboard writing style.
I have to image whatever style of writing this was trained on is a lot more pleasant to read and I feel bad for whoever writes like this now being associated as bad AI writing.
most of this stuff isn't really possible at this point _without_ models, so pissing and moaning about people publishing work and not humanizing is really silly.
I used similar prompts before. Now I simply say to "remove historical cruft" and results are good enough. It's the model itself that first used this wording, I found it concise.
thanks! I'll do!
I have a 48GB M5. I don't need to run larger models. I want more context. I've managed to set the context window at 71,680 using Qwen3.8-27B-oQ4e-fp16-mtp. But I want more. Is anybody, with similar specs, able to set their context window higher?
We are running 35b-A3b with 264k context (the model's default max) using vllm and the "frog" jinja templates: https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates and had good luck. We are mostly running agentic workloads though, rather than coding. 27b has a slightly higher agentic job completion rate (95% vs 92%) but the 3% trade off is worth it because the A3B is sooooo much faster, and we reprocess the other jobs with a different model. Don't sleep on the froggeric templates.
Qwen: Looking at you for a new ~35B MoE! Please and thank you
I am running 3.8 27b at q6 quant with 160k context on a 32gb video card (arc b70 pro) - I quantized the kv cache at q8 - that is the only trick really - works great.
yes, with qwen3.8-27b-4bit run via rapid-mlx i can get to about 200k
Nice.. and how many toks/sec?
You should try Glimmer MTP. Qwen3.8 27B seems to have weird memory and caching issues on oMLX
It's hard to believe 16GB unified memory will give you 5 tok/sec unless you are ignoring the thermal warnings. I am running Qwen3.6-35B-A3B on my 16GB M3 and get 7-8 tokens/sec with all the optimizations while keeping the peak memory and thermal warnings at check. https://github.com/deepanwadhwa/samosa-chat
Now I'm feeling pretty good about getting 10-11 tokens/sec running Qwopus 3.6-35B-A3B Q6_K on an old Mac Pro 2013 (trashcan) with 128GB RAM (DDR3), 12 core Xeon, dual D700s. Arch Linux and llama.cpp.
haha, good for you.
Anything smaller than a 16” runs into serious thermal problems; even an identically equipped 14” just can’t dissipate enough heat.
interesting! Yes, thermal is important. Pretty cool project man! Starred and checking it out!
Not a mac/UMA discussion point, but is it time to add additional, installable, DDR5 to GPUs? I can see this as a win/loose. PCIe 5x16 is close to maxing out the bandwidth available from high end dual channel DDR5 now, but not quite. I'm not a hardware person but I suspect putting it on the card could lead to significant performance improvements over using system ram so allowing systems like this, where MOE weights are shed, to get even higher performance than just adding that DDR5 to the system. Bigger models become closer to reality and it provides more of a pathway for developing technologies that take advantage of it. Of course the loose side is that you just put a lot of specialized ram on a card instead of into the system where it could be used for other things. I could see a place for a 16GB card with 64GB(or more) of DDR5 especially if we start seeing MOE and similar technologies really start being designed for this concept.
I'm hoping to see progress in this space.
Folks talking about how 32G is not enough for local use, but then there's been work like this to empower it.
My hope is that the new 32G M6 will be "useful" locally, possibly because of work like this.
Yes, but also 12 tok/s versus Claude is so far from comparable. I know that it’s not exactly 1:1, but it’s a long way from an easy trade-off, especially considering hardware prices for high levels of RAM.
32GB is simply too tight; you need 8 minimum for the OS and you need about 4-8 more for the LLM you’re visiting and kv cache.
yes! I'm bullish on this. there is a lot of work to do. I've been experimenting with pruning, distillation, and retraining too. I'm sure your 32gb m6 will run a badass local model!
As someone who is just looking at the theoretical benchmarks of each of these models I'm curious if anyone could share what are the problems (maybe around code) that flash-next was able to solve which 27b was not able to
For a local non-coding agent, instruction following and tool use are the most important gains
This is the best I could find: https://huggingface.co/Qwen/Qwen3.8-Flash-Next?utm_source=ch...
About the specifics, I have only anecdotal evidence, but I guess this info can be found somewhere
I love these efforts to get proper models running on lower cost hardware and I think this is where the next real breakthrough will come from. The more efficient this sort of thing can be done the bigger the chance to democratize this tech, 'good enough' is what you need and as long 'top of the line' gives a competitive edge even if it is at a cost there is a substantial risk of the door closing on general computing at some point in the near future. Keep in mind that there is no guarantee that the pendulum has to swing back, it can swing one way and get stuck, and then you're going to have to beg for crumbs from the haves.
I think there's a very good chance that history will rhyme a bit.
DOS/Windows and PC clones were by no means the best available, but they were cheap, ubiquitous, and versatile compared to alternatives that were either much better at one task but more expensive or better at everything but wildly expensive. They were "good enough" and represented a solid improvement over what many existing computer users had as well as a good entry point for new users. As such they spread like wildfire and became the standard while the expensive alternatives either became hardcore niche or vanished.
SUN Apollo SGI
Though to be fair it was Linux more than Windows that killed them. Dos and Windows were competition for DEC and - ironically - IBM.
so many inference project, omlx already supports all of this and has a 1000 people trying to optimize it constantly
Both projects are different in scope. Think of slotstream as optimizing for memory and for this specific model for now, my intention is not to build an inference engine the same as oMLX
Interesting! I'll check it out
32GB dedicated to an N-gram table instead of a draft model is an unusual choice for speculative decoding — what made it win over the more common draft-model approach here?
how much energy does it consume?
Good one! I haven't measured this. I'll include it!
"Disk is the gate that bites first"
AI;DR
Ha! AI;DR is a great phrase. Had not seen that before
The never ending gate bites.
How I have come to detest certain phrases.
Load bearing gate bites.
...the seam.
Interesting! You're absolutely right, I'll check
It seems we could use a new kind of memory that streams the weight data in, like GDDR in reverse.
High Bandwidth Flash? https://www.sandisk.com/company/newsroom/blogs/2025/scaling-...
Optane? (Too soon?)
Optane was targeting the latency gap, while HBF targets bandwidth. Given how LLMs work, HBF is perfect for offloading.
interesting!
yes! I guess future hardware designs will have something like that!
How usable is 12 tok/s?
Unpleasant for interactive agentic work. Still useful to leave it to do some work in the background.
Next help us normies run GLM 5.3 on our potato computers. Wouldn't that be nice!
Is this going to destroy my SSD?
no it's reading, not writing
Using macos on low memory regimes will make it use disk-based swap.
For example, a Macbook Neo (so in theory, something with around 4GiB of free RAM lying around) might eat around 900GB of writes a day while not doing much at all, because it's basically on low on RAM and swapping all the time.
A particularly worrying situation considering a dead SSD will render your macbook usable for parts only.
By reading it?
Reading causes insignificant wear ("read disturb") that likely isn't a problem, but I don't think it's possible to issue pure reads to modern SSDs. The NVMe spec mandates tracking the amount of data read, and this has to be written to the drive. I'd hope the firmware buffers this and writes it at low frequency, but on the other hand, I doubt the firmware was tested in extreme random-read regimes. Unexpected failures from excessive statistics recording could be possible.
I don't know actually. I'll check haha. My best guess is it isn't.
I hope not! this is a new macbook lol!
I swear the models are named by the beatbox aliens from the post office in MiB
There are already a handful of repos doing essentially exactly this: `mlx-moe-offload`, `streamlx`, `mlx-moe`, `mlx-flash`, and `deepseek-v4-flash-mlx` - i.e. keep the resident parts of an MoE in unified memory and page/stream routed experts from SSD on Apple Silicon.
At this point I'd much rather see people collaborate on one of these implementations, benchmark against them, or upstream the useful bits into MLX/MLX-LM instead of producing yet another near-identical repo.
The local-LLM ecosystem really does not need every implementation idea rediscovered five times and wrapped in a new README. AI-assisted coding makes producing a new repo cheap; maintaining, benchmarking, and integrating one is the actually valuable part.
I see your point. As an oss defender myself, I agree, however, the spirit of this is to see how fast I can make it. I'm sharing this with the community, which I think is aligned with the original oss spirit.
It's an experiment for myself but I am committing to maintain it. I've been an oss person for a loooong time, way before AI was a thing. Think about it as a new, from-scratch take at it, not as a re-reproduction.
Hey carloslfu, kudos from the other side of the internet, don't get down on people nitpicking everything here, experimenting and discovering is part of learning so keep going!, remember this is the place that said dropbox was dumb and could be replaced by a script.
I think multiple people working on the same thing is great.
Everyone comes at it from a different point of view, and some approaches work, some don't. And when people do this themselves they learn. Existing projects have their mistakes worked out already.
Maybe one of these people is going to come up with the thing that nobody else thought of because of their experience working the problem from scratch. You may not get that from someone working from an existing project, because existing projects have their approach "baked in."
What all these projects are showing so far is that it's possible to stream from disk, but that the performance isn't ideal. But I'm sure you could take this approach with smaller models and get better performance.
In addition, it's a given that when you work with large data sets performance means organizing the data to take advantage of caches, both disk and cpu. It's not clear how that would work, exactly, given that each run is a not-quite-random walk through the data. The Big Data way is to prebuild all of that as much as possible, which is probably impossible with a big model. But what about a smaller model?
true
This is one of the aspects of this year that I've been finding very grating and wasteful. Collaboration still happens among people with the ability to do so and the technical skills, but everyone else is taking their own helicopter to the top of the mountain, "putting it out there", and there's just a ton of redundant projects that do the same thing.
It's horrible. Every 20-something working on a load-bearing inference engine on GitHub.
for the record, I'm almost 35
Vouched especially since OP might have a perspective on this. And readers may want to look up those other repos and compare for themselves.
Thanks for the feedback! I'll create a section with a benchmark and comparisons. This will hold the project accountable and speed things up imo
Absolutely! Nice.
(The comments under the parent indicate it was improperly flagged/made dead (maybe could happen just from downvoting?) so glad I hit the Vouch.)
Why should they do that? For you? You could merge those projects and see if they get traction.
And there are `Mference` and `SwiftLM` too, I think they are doing the same use case.
AI NIH
Sorry, I don't get "NIH". what's that?
Not Invented Here.
Ah! Yeah, I didn't invent anything (yet!). The goal is to see how far I can take it in terms of speed without consuming that much RAM.
I'm only joking anyway — it's more a comment on the whole AI-accelerated trend of everyone having their own version of a thing.
I do agree that, ultimately, combining your efforts with others working in this whole area is probably really worth it, but I can see how there's an ease of pushing forward on your own these days.
I do not have fast internet so I am not sure when I'll really be able to download the weights but I do have an M1 Max to try this on, so I will at some point!
I see! yes, downloading the weights part is painful. I tried a couple fixes and it is as fast as it can get downloading from HuggingFace. I think the field is heading toward smaller, more capable models soon, so you won't have to wait that long!
I mostly use Muse Glimmer, which runs quite well on my M1 Max as it is 30B but it also reasons very efficiently. I have tried the Qwen 3.8 27B which is also usable if quite slow to reason, but I guess like everyone the idea of the Flash-Next model holds some intrigue, because the 35B-A3B is pretty good.
> every implementation idea rediscovered five times and wrapped in a new README
That's open source since forever, unfortunately.
I agree with the sentiment, but have you seen those videos in which all men say other men are gay? This feels like the same, so much AI paranoia!
I genuinely want to contribute. And hey! I was doing oss this since 2014 so waay before AI was cool.
It's what happens when you don't do market research.
I'm sorry this makes it seem like I didn't do my research. I did a TON. To fix it I'll add a benchmark/comparison table. Also, I wouldn't call it market research since this is not commercial AT ALL.
Hey Carlos, thanks for sharing with the community! Appreciated
thanks!
Does a painter check to make sure that a portrait hasn't been painted? What a dismissive comment.
Half the people on here are using Ollama. No one is doing market research.