posts
- 115 llama.cpp commits later, my 3090 was barely faster
I checked 115 new llama.cpp commits on an RTX 3090: tool calls held, speed moved 2.8% at most, adaptive MTP gave nothing. I switched anyway.
- Faster prompts, slower agents: a second engine on the 680M
A MoE engine read prompts 1.4x faster than llama.cpp on a Radeon 680M and generated slower. Three agent runs got the same grades and finished later.
- The template differed. The prompts didn't.
Two quants of one 122B model shipped two chat templates. I held the template equal, then checked whether it had ever mattered.
- 105 identical commands and other ways local agents fail
1,655 agent transcripts from a 3090 bench: killed runs wrote longest replies four times those of passing runs, loops never passed, edits missed.
- Most models passed the scripts. The ledgers split them
Which local models passed which coding tasks on one RTX 3090: nearly all wrote the small CLI tools, and no model passed every Spring Boot run.
- On one RTX 3090, agent speed followed active parameters
Generation speed of 28 local models driving coding agents on one RTX 3090. Active parameters and MTP drafting set it; the quant moved it far less.
- Twenty-one agent runs on a laptop iGPU, none merge-ready
Seven local MoE models ran three coding tasks each on a Radeon 680M. A blind review graded all 21 runs and found none merge-ready.
- On a laptop iGPU, memory bandwidth picked the model
Coding agents on a Radeon 680M: a dense 27B generated 5.7 tokens a second whatever I tried; MoEs with 2-3B active on the GPU, 27 to 48.
- My drift check said 0 differences. It checked 4 arrays.
My Go checker read a bash model config without running it and said 0 differences. It compared 4 arrays. The fail-closed rewrite found a missing output cap.
- My agents got their own config. Two still read mine.
A separate config dir per coding agent kept my files untouched. Claude Code and OpenCode still read my ~/.claude, and OpenCode sent a prompt to a hosted model.