Same here, I use Qwen 3.6 27b (Q6 quant) with llama.cpp on an RTX 5090 using the pi agent exclusively now. The fact that it's local means that I never have to think about token pricing, quotas, time of day, or data sensitivity. I have limited the GPU from 600W to 450W which means the system stays whisper quiet during inference.
I have become so "lazy" (in a good way), so far that I've started using the model for lots of daily mundane things on top of just coding:
* "commit this on a branch, push, create a PR and assign $nickname for review"
* "Use the Stripe CLI to download all open and overdue invoices and reconcile them with this CSV export from our bank account."
* "Use these Elasticsearch credentials to summarise what kind of operations are causing load at the moment."
* "Tell me if our codebase already supports X and where it's implemented."
No KV cache quant, context length 50% of original, MTP absolutely. These are the relevant cmdline attributes. Getting around 100t/s with this setup, even when watt-limited to 450W.
Not the person you asked, but I have a 9700 which has the same VRAM, and running Q6 on it with unquantized kv gives me 50k context. Putting -ctv q8_0 ups that to 70k. I normally run Q4 with unquantized kv @ 130k at 50 t/s (mtp 3), with the disclaimer that I'm running PCIe gen4x8, so I'm slightly slowed. I've found that quantizing k leads to broken json on tool calls, which is fairly unrecoverable, but YMMV.
I have become so "lazy" (in a good way), so far that I've started using the model for lots of daily mundane things on top of just coding: