Posted by snehesht 9 hours ago
Surprisingly useful as long as you can leave it running a couple of hours at the very least.
While huge models will still be better I think the general availability of RAM might be the downfall of AI companies.
Everything else (weights) are shared amongst tens of thousands of users currently doing inference in that cluster, so even if there are terabytes of weights for the model, they aren't much on a per-user basis.
Where's that figure comning from? Last time I checked (could be the 3.6 Qwen 27b) single token needed 32kb
You know, so you're not wasting your time like in this post.
- Hard to benefit from thinking and preserve thinking given token cost.
- Low quants reduce accuracy heavMTP draft can make it make the same mistakes all the time when calling tools, formatting output or following basic guidelines. Otherwise 2x-4x slower.
- K/V quants probably quantized too make things less accurate.
Useful would be combinations with:
- Full context size so it can code and think a bit.
- Draft MTP <= 2 so it doesn't trip
- Q4 quants or better so its accurate
- q8 cache or better so it stays accurate.
- 20 token/s so it finishes while reviewing previous step.
- 1000 tokens/s context load so compactions don't waste 10+ minutes.
- And enough left RAM for 50+ context checkpoints so that it can progress quuckly.
Closest you have is Qwen3.6-35B-A3B-MTP.
Latest gens (Qwen3.8 and co.) are just too big for low specs. 27B dense models seem to be ok for integrated >=92 GiB RAM.
Source: I have low specs and tried them all for agentic use + coding.
Their marketing made it look like a breakthrough, but in my experience it’s just the next step down from the Q2 quants in both size and quality.
Q2 quants are already not very useful in my experience. The Bonsai models are even worse.
If you only need 80% plausible outputs that don’t need to reference a lot of context they can be useful. If you try to use them for real tasks it feels like time warping back to 2023 when you LLMs were barely useful if you babysat every word of the output.
> Draft MTP <= 2 so it doesn't trip
I am not sure you understand what either of these things do.
Do you think that FA or MTP are lossy?
My experience is that draft-mtp=2 gives 25% improvement in tokens/s but the model is unable to call tools with the right arguments reliably. I have since then gone for smaller quants at draft-mtp=1 and that problem is at least gone so far.
An agent needs to repeat tool calls in the right way, so cannot penalize repetition. This however leads to the agent retrying the wrong calls constantly.
There's a lot of good information here but the comment spoils itself by coming across as aggressive in this way.
This is particularly a problem when responding to someone else's work. We need commenters to point out problems respectfully, not put down what other people have been making.
I’m all for local models and I do want them to be the future but I wonder when, and if ever, we’ll catch up to a level of, let’s say Opus 4.6. I guess it’s currently doable but requires $50k hardware?
I've been doing local inference for a couple of years on the side, and I'm astonished at the number of variables you need to have control over to get a reliable result. Inference engine, model parameters (top_k, temp, MTP-enabled/not), quant level, and harness all have a big impact on the results.
DS4-0731 at 2bit on llama.cpp (ROCm) and 250k context with omp.sh has been consistently reliable for me, just a bit slow (10 t/s) compared to what I'd prefer. Trying out DwarfStar today (benching it right now) to see if I can get better speed, but otherwise I've found it to be great on my side projects that are smaller (up to 10ksloc).
There could also be a domain issue - I tend to do lots of web programming and sysadmin work in these projects; if your work is more esoteric, it might not be nearly as good. I haven't tested much outside of my narrow domain.
Qwen 3.8 Flash Next is there. 3.8 27b is fairly close.
I'm excited to see what Qwen 4 will bring.
I'm running on a 128GB Strix Halo for Flash Next and an Intel Arc Pro B70 (32GB) for 27b.
I think there's probably low-hanging fruit to outsource reasoning from rote read/writes.... Just speculation though, I'm not a token optimization expert.
What speed are you willing the sacrifice to debug/program for more complex jobs faster?
Then there are also these quants; https://huggingface.co/IsValorum/Qwen3.8-35B-A3B-Distill-MLX...
With Flash Next you only have ~6B active parameters so you can toss experts up into VRAM and/or run them on a CPU if you have enough RAM and bandwidth.