Llm

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. The weights would not all fit in VRAM, so I needed a setup that could offload them.

I use big LLMs to draft blog posts and LinkedIn posts. They help with research and structure, but I still spend time rewriting them to sound like me. Every draft comes out with the same polished, slightly corporate voice, and even after I edit it by hand, something of that voice stays in the text. There is actually research confirming this: a 2026 study on post-editing found that even when humans edit LLM drafts, the result stays measurably closer to LLM style than to the author’s own writing.