I recently picked up R9700 to run Qwen 3.8-27B.<p>1) I have knowledge DB that I query, spend a lot of time to get "query N models, stream to me all N results, let me pick which one" - only to find out that qwen beat all other contenders (those were picked before I got R9700, so the rest is =<8B parameters)<p>2) Setup OpenCode to use it and gave it a few tasks:<p><pre><code> - first task (new feature to my MCP) took 40 minutes to complete with zero input from me, while it took 20 for sonnet-5 and sonnet-5 kept bugging me. Results are near identical.
- second task (port a specific version of a package to my flake) it got stuck in a hilarious loop where model already been told what hash to use by nix itself, but it wanted to figure our how to get hash another way for some reason.
- third task (another task, but much harder than first one with most of the discovery already done), kept doing discovery and running out of context, went through 3 compactions (256k context is what I can fit on R9700). Room got too hot, so I stopped it.
3) virtual assistant like Hermes but my own: no notes, works great.
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I'm pretty sure codding issues are just harness and lack of memory that Claude Code already had. Pretty nice setup, similar to mine but I built my own lightweight PaaS that is highly specific to what I run.
I have a similar setup to the OP (although I'm not sure if I'd call it a "software factory"), and I utilize a local model for some aspects of the setup. Specifically, I have a single RTX 3090 Ti with 24GB VRAM on my (main) home server, and I've been primarily running Qwen 3.6 35B A3B out of it via Ollama (I just switched to Qwen 3.8 27B, though, and I've also tried other Qwen models as well as Gemma models).<p>The Qwen models are decent, but they don't come close to the full Claude experience I've come to expect. As such, I only use the local models for specific tasks where it makes sense to do so. Really the setup is that my Claude-powered agents are able to incorporate my local model into work it builds out. The agents can perform inference against the Ollama API as they see fit, and I encourage them to do so for tasks where (a) the low-level capacity of the local models make sense and/or (b) where costs can become a concern.<p>It seems to work well when it comes into play (like having Claude drive a web browsing session but letting Qwen handle much of the actual browser interactions, image analysis, etc.). Still, Qwen just isn't smart enough (or fast enough on my machine) to handle anything agentic that isn't non-trivial.