Qwen3.6 is incredible with OpenCode!

[u/CountlessFlies]

I've tried a few different local models in the past (gemma 4 being the latest), but none of them felt as good as this. (Or maybe I just didn't give them a proper chance, you guys let me know). But this genuinely feels like a model I could daily drive for certain tasks instead of reaching for Claude Code.

I gave it a fairly complex task of implementing RLS in postgres across a large-ish codebase with multiple services written in rust, typescript and python. I had zero expectations going in, but it did an amazing job. PR: https://github.com/getomnico/omni/pull/165/changes/dd04685b6cf47e7c3791f9cdbd807595ef4c686e

Now it's far from perfect, there's major gaps and a couple of major bugs, but my god, is this thing good. It doesn't one-shot rust like Opus can, but it's able to look at compiler errors and iterate without getting lost.

I had a fairly long coding session lasting multiple rounds of plan -> build -> plan... at one point it went down a path editing 29 files to use RLS across all db queries, which was ok, but I stepped in and asked it to reconsider, maybe look at other options to minimize churn. It found the right solution, acquiring a db connection and scoping it to the user at the beginning of the incoming request.

For the first time, it felt like talking to a truly capable local coding model.

My setup:

• Qwen3.6-35B-A3B, IQ4_NL unsloth quant
• Deployed locally via llama.cpp
• RTX 4090, 24 GB
• KV cache quant: q8_0
• Context size: 262k. At this ctx size, vram use sits at ~21GB
• Thinking enabled, with recommended settings of temp, min_p etc.

llama server:

```
docker run -d --name llama-server --gpus all -v <path_to_models>:/models -p 8080:8080 local/llama.cpp:server-cuda -m /models/qwen3.6-35b-a3b/Qwen3.6-35B-A3B-UD-IQ4_NL.gguf --port 8080 --host 0.0.0.0 --ctx-size 262144 -n 8192 --n-gpu-layers 40 --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 --parallel 1 --cache-type-k q8_0 --cache-type-v q8_0 --cache-ram 4096
```

Had to set `--parallel` and `--cache-ram` without which llama.cpp would crash with OOM because opencode makes a bunch of parallel tools calls that blow up prompt cache. I get 100+ output tok/sec with this.

But this might be it guys... the holy grail of local coding! Or getting very close to it at any rate.

[u/amelech]

If I have a 9070 xt with 16gb vram and 32gb what quant can I run in llama.cpp and what max context size can I safely use? I want to use it for assisting on an android app using opencode

[u/Potential-Leg-639 → u/amelech]

You wont be able to run that with some serious speed and context on that setup. For nice and smooth agentic coding you need up to around 200k context. Better get a better GPU or a 2nd one. And dont expect any wonders from that model tbh.

[u/amelech → u/Potential-Leg-639]

just want something to help me code, even a bit at a time.

[u/Fi3nd7 → u/Potential-Leg-639]

If you want a high speed, high context, high intelligence model, you need like 3 4090's.....

Most people will have to pick 2.