I put Qwen 3.6 27B, Qwen 3.6 35B-A3B, Qwythos-9B, GLM-4.7-Flash, and Nemotron-3-Nano through the same real coding task on my homelab RTX 5090. Along the way I had to live-patch two separate llama.cpp bugs — and even after fixing them, I couldn't fully prove one model's failure wasn't the harness's fault.
A gloomy Cape Cod afternoon post-July 4th turns into a deep dive on ComfyUI, Lemonade Server, and LocalAI — plus llama-benchy and AMD's AI Playbooks — and the case for a bakeoff against our hand-tuned llama.cpp stack.
GLM is suddenly everywhere in developer conversations. Before we run the bakeoff, we need to answer two questions: what is GLM, and is it suitable for a single RTX 5090 homelab?
I gave five local LLMs and one frontier cloud model the same coding task on my homelab: build a tag manager for the blog's admin panel. Only two shipped anything. Here's what happened.
Four frontier models, ten tasks, one government shutdown. We ran Claude Fable 5 through the homelab benchmark harness three hours before Anthropic pulled the plug — and it came in second. Here's the full bakeoff.
The Round 5 bakeoff produced four implementations. None of them shipped. What shipped was a merge of the best pieces from all four, then a polish pass against real data. Bakeoff → Merge → Polish is a generalizable pattern for any feature where the design space is genuinely unclear.
Four LLM models built the same admin feature in isolated Coder Agents sessions. I judged them blind. The headline result: Sonnet 4.6 beat Opus 4.6 on a coding task. The deeper story is what each model did with the same prompt — and what it took to make the bakeoff fair in the first place.