Show HN: Running 104GB Qwen3.8-Flash-Next on 48GB Mac with at ~12 tok/s

I tried this in my mac mini m2 16GB, unfortunately I have to use an usb disk for the model weights, and I’m getting 0.5 tok/s. Still, being able to run (heh maybe crawl is more accurate) a 100B model on this computer AT ALL is pretty cool.I see disk maxing out at 400 MB/s, this disk should be able to hit 1GB/s (it hits that eg when verifying the check sum of the weights), so there might be some optimization to be done there (I’m guessing it’s because the weights access is not pure sequential reads but involves some randomness depending on which expert)

Brilliant! Have you had any success integrating it into a MacOS Swift app. I'd love to see it in action before I consider adding it to a future build. My biggest issue is getting these opensource models to use tools well enough for production.

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 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 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?

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

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

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.

> 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".

"Disk is the gate that bites first"AI;DR