Local-Llm

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?

I use big LLMs to draft blog posts and LinkedIn posts. They are great at research and structure, and terrible at sounding 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.