3 ms·
I've been testing Ornith-1.0 35B (my own FP8-block quant) and I like it. It runs at >200 tok/s w/ vLLM on an RTX PRO 6000 (sm120), I've run >140M cached tokens
by lhl 3mo ago
I've been testing Ornith-1.0 35B (my own FP8-block quant) and I like it. It runs at >200 tok/s w/ vLLM on an RTX PRO 6000 (sm120), I've run >140M cached tokens of agentic coding work on it over the past few days. It seems to about somewhere between Qwen 3.6 35B-A3B and 27B, but the good thing: it overthinks/doom-loop a lot less than Qwen 3.6. When looking at the thinking traces I like its breakdown approach template.
It does good job on basic analysis, tasks, and some front-end/backend changes on a medium-sized Go codebase, but it reached its limits totally botching a longer (simple) kernel implementation job (about 100 iterations in Pi Agent harness) - this is the type of thing that stronger open models (Kimi K2.6, GLM 5.2) are able to do.
- regularfry 3mo agoWith this model size I've found that the harness seems to matter more. I've moved on to little-coder rather than raw pi with qwen3.6 27b personally, it might be worth taking a look.
- lhl 3mo agoYou got me curious, so I made a little harness comparison to my model test suite: Model Adapter Suite Score Passed Tasks --------------------------------- ------------ -------------- ------ ------ ----- local/ornith-1.0-35b little_coder aider_polyglot 36.0% 81/225 225 local/ornith-1.0-35b pi_devstack aider_polyglot 39.6% 89/225 225 local/ornith-1.0-35b pi_vanilla aider_polyglot 32.0% 72/225 225 Little Code does a little better than raw Pi, although maybe not better than my personal Pi setup: https://github.com/lhl/devstack https://github.com/lhl/devstack