I’m all for local models and getting away from Anthropic/OAI but Qwen 27B might as well be a horse drawn carriage compared to a 2026 Porsche. Like, I guess it’s useful if you had nothing better but acting like it’s comparable to anything the frontier models have is just disingenuous.
bro, you are looking at a repo that shows you exactly how to use it to achieve better results than a frontier model
disagree, it’s absolutely comparable. but an m5 max is where it shines. and there are 50 flags to finetune with llama server before you get it right. i do prefer llama server over mlx currently on mac as well.
It’s so poor for most tasks that you’re faster doing it manually.
DeepSeek and GLM are useful, though. Even the flash variants.
I have all three and you're not going to convince me of that.
Side-by-side Qwen 3.8 27B is ~6x as fast as DS4 and GLM 5.3 and faster than that if you get the speculative engine set up properly. That means you can iterate very rapidly on the same problem and for many problems this is the way to solve them. DS4 and GLM 5.3 shine when Qwen would take too many turns to solve something, they simply have more world knowledge and seem to home in on possible solutions faster in #tg but slower in wall clock time. If a particular problem is within striking range of all three then Qwen will usually be the fastest solver by far.
It also depends on how good your prompt is, whether you are setting up points for guidance or whether you expect everything to be solved in a single shot.
> Side-by-side Qwen 3.8 27B is ~6x as fast as DS4 and GLM 5.3
Wow, that sounds like a lot! Do you use/mean the regular models or flash variants here?
Specifically:
The GLM Quant is ~4 bpw, the DS4 is a Q8 and ninfer one is an NVFP4/BF16 I think. I run the ninfer one with ninfer-4090 on a hacked card with 48GB of VRAM. Max context for that one is 256K, DS4 has a 1M context and for GLM 5.3 it is 300K. The big models use all 8 GPUs.Qwen benefits in two ways: all of the VRAM is on a single GPU so there is no communications other than generated tokens and new activations, that gives pretty good utilization for the card, the second is of course simply the fact that it is a much smaller model so there is less to compute. The 4090 is still power limited so it could do more but it's loud (and hot) enough as it is.
More details here:
https://jacquesmattheij.com/local-inference-with-large-langu...