Qwen

In Part 1 of this series, we set up Qwen 3.6 35B A3B with FreeToken on an RTX 3060 (12 GB VRAM), clocking a reliable 65 to 70 tokens per second for daily coding loops. In Part 2, we ran 640 public trials and 280 private trials benchmarking terminal coding agents, and found that 35B models solve everyday bugs and features with great reliability.

Then came the inevitable follow-up question: what is the absolute ceiling on single-card consumer hardware? Can we actually run a frontier-scale 100B+ MoE model on a $280 graphics card without renting a cluster or buying four GPUs?

This is Part 2 of a three-part series on running, benchmarking, and scaling local coding agents. Part 1 covers the local Qwen setup; Part 3 explores running a larger MoE model on the same desktop hardware.

In Part 1, I got Qwen 3.6 35B A3B NVFP4 running locally on an RTX 3060 with a 64k context window and wired it into Crush, Pi, and Oh My Pi. I wanted to know how that setup compared with the terminal coding agents I already had on subscriptions. Could it finish the same tasks, and would I want to merge the patches?

This is Part 1 of a three-part series on running, benchmarking, and scaling local coding agents. In Part 2, we benchmark this setup across 640 public trials and 280 private trials on a production Go monorepo, including Claude Code, Codex, Antigravity, local Qwen, and two hosted models. In Part 3, we build and test the specialized llama-moe-cache fork to run 177B Qwen3.8-Flash-Next on desktop hardware.

I wanted a capable local reasoning model for coding agents on my workstation, but my GPU is a standard desktop NVIDIA GeForce RTX 3060 with 12GB VRAM. Running modern 30B+ coding models locally usually demands either punishing quantization trade-offs or multiple datacenter cards.