Posted by snehesht 11 hours ago
It is more useful than Qwen3.8:27b (which is already quite good) and runs faster on my 7900 XTX / 64 GB DDR4 system.
Local LLM is getting more exciting every day!
That's the exciting part of this - before the best you could run on <24gb vram was qwen3.8-27b at q4 quantization. Now you can run a nerfed 125B parameter model on under $800 of hardware, and it beats a less-nerfed 27b model.
The Readme doesn't say, but it's all AI generated, so..
amazing project, congrats on the launch
I think publishing benchmarks with quantized models should become standard practice.
We observe that such degradation varies substantially across these factors: 4-bit quantization usually preserves performance, 2-bit often causes broad degradation
> Coder: a coding version with half of the experts removed. It reaches 91% of the full model's SWE-bench Verified score (measured by its authors) and fits 32 GB of RAM.
https://github.com/Niko1221/Strata#which-model-should-i-pick
27b at q4 is ~16gb
So from a raw amount of data, qwen3.8-flash-next wins easily. But flash-next is an MoE model, so it only has 6b parameters active per token, vs 27b's dense 27b per token. So 27b@q4 uses ~16gb of weights per token, and flash-next uses about 4gb of weights (125/80 * 6).
But those numbers don't really tell us anything useful, because there is an interplay between total model size and active parameters and intelligence that isn't obvious or simple.
(sizes are based on the unsloth quants, not the coder variant, but the idea holds - this isn't calculatable with simple math, you gotta test them and see)
Holds up pretty well
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.
Q1 was producing some garbage at times, generating wrong urls on webfetch, then convinced itself there was some url rewrite in the middle. With IQ2 it happened much less but still happened, and once it would all webfetches became like that. IQ3_XXS is the maximum I can run: I don't have problems anymore, though I have less available context window.