llm
- Image input on a 27B coder costs 1.1 GiB, once
Image input for Qwen3.8-27B coding keys on an RTX 3090 cost 1.1 GiB at load on every variant. A full 121k-token context with a screenshot added 118 MiB.
- One open PR made MoE prompts 12-30% faster on my 3090
115 llama.cpp commits gave my RTX 3090 2.8% at most. One open PR from Unsloth's fork made MoE prompts 21-30% faster in llama-bench, 12-13% in llama-server.
- 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.
- 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.
- The third run mattered where it cost the most
739 three-run groups from my agent bench: 19% split on basic tasks, 56% on Zig. So basic drops to two runs, and Zig runs only after the hard tier earns it.