alt Hacker NewsIf you have a 24-64GB mac, consider running Qwen3.8 27B locally at night. It's a bit slower to run locally, but if you're sleeping it's less of a problem.
Depending on your memory, you'll need to use the weaker Q4 versions but they still perform well.
It ranks higher than GPT-5.3 Codex (xhigh) or Claude Opus 4.6 (max) so is great for pairing with https://github.com/kunchenguid/gnhf for nightly experimentation, cleanup, or recommendation lists for in the morning.
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I have all sorts of local compute, and local models fairly capable the Frontier models still way more capable/faster and local electricity consumption is something else. Good thing it is getting colder around here.
I often pair them up, and I have an Astra or Sol work as a supervisor and reviewer while Qwen 27B FP8 or Qwen 3.8 Flash Next implements things. I mostly do it as an experiment, just to see what kind of level of autonomy I can get, and they are slow to getting a decent reviewed outcome despite Qwen27B running at 100+ tps and 3.5-4K prefill rates and Qwen3.8 Next at 40 tps and 1-2K prefill. I've been also using similar approach more with OMP, not just the straight Pi harness. And OMP seems to be slower because it has more guardrails. OMP has an interesting feature where you can assign a better LLM as an advisor, wehere it just sort of monitors the progress and injects guidance. And it definitely helps, but one has to be careful. It actually turns out to be expensive if the cache reads are expensive. I learned it the hard way. Where on Fireworks' API, the cache rates for GLM 5.3 flash are quite a bit more expensive than for DeepSeek, and a simple runs ended up costing me three bucks in oversight. So a better way is to have a Frontier model running a separate tmux pane and just directing it to Wake up every 10 minutes, take a peek at what's going on, review the milestones give feedback and then sleep. This turns out to be pretty decent cost saving strategy when quote needs to be stretched. Paradoxically, OpenAI tightening up their quota allowance once they released Astra actually pushed me into all these sorts of experiments, and it's actually been interesting. I've been exploring all these smaller flash models, and it's been nice. I do like using local LLMs for chore type tasks that are just mostly information gathering, post-session reviews, stuff like that.
On my 64gb m3 max qwen3.8 27b has been great for planning and then letting qwen3.6 35ba3b actually implement the planned changes.
If you're on MacOS, with atleast an M3 chip and 32GB, you should look at the splash engine.
GNHF seems exactly what I've been aiming for to handle overnight tasks.
It's _so_ good, I no longer bother with Sonnet and use it locally for everything.
Consider bumping reasoning down to Medium as a default though, I agree with simonw it over thinks https://simonwillison.net/2026/Aug/16/qwen-38-27b/
What would you personally recommend for those that have 128 GB?
You might be interested in https://prismml.com/news/bonsai-2-27b
I still haven't found a use case for Qwen3.8 27B that Qwen 3.6 35b A3b (MoE) is better at. I can get at most 40 tokens/second with 27B, but I get around 90 tokens/second with the MoE and it seems to be a more capable model.
I guess I should still keep experimenting though. Maybe I'm just not using a dense model correctly.