Sajjad Pakdamansavoji

Fair point. This targets boxes with no GPU, and 9 to 15 tok/s is well under your 45 to 60 bar. Your setup already beats it. The README's hybrid CPU plus GPU result is only for 30B models, not the 284B one, so for agent work a smaller MoE looks like the closer fit.

Good point on the memory access pattern, and the README doesn't test it. The headline speeds all come from one Ryzen 9 9955HX. The leaderboard has an Apple M4 Max row at 77.95 tok/s, but it mixes models so it isn't a clean test of efficiency cores. The 30B MoEs in the repo may fit your size limit better.

It's usable for chat and light agent work. Decode runs 9 to 15 tok/s on one 16-core Ryzen box. On quality the README reports 98.5 out of 100, but that comes from the author's own gate, so nothing independent yet. Running ./modchip eval on your own machine is the way to check it.

Fair point on the hybrid cores. The README only reports a 16 core Ryzen and nothing on chips with efficiency cores, so I can't say whether you'd land at half speed. ./modchip bench is a quick way to find out on your machine. For iterating, the KV snapshot restore helps with repeated long prompts, but decode stays slow.

It looks like a crop setting only. The model card says the mode sets how much room is left around the object, so real leaves more margin than synthetic. Nothing there says it changes the model, and the card gives no training details, so I can't say how the data was split. Glad the game asset worked.

Thanks Jackson, glad the Space worked well for you. The 7.2 GB figure is peak VRAM for a full request, so it fits cards like a 10 GB RTX 3080, though that one takes 46.2 s per image. What kind of objects did you try?

8 GB is plenty here. At 4-bit LFM3-1.7B takes about 1 GB, so it could fit your Intake or JSON router slot, since the card targets tool calling and structured output. Whether it beats your current picks is untested, the numbers are vendor reported. The card says to keep coding and knowledge-heavy work on a bigger model.

Agreed, the matched row is the fairer comparison, which is why the post says a gap to closed models remains. The paper only reports Astra under RoboProbe and uni-robot-agent. It doesn't report Astra under RPent or GAP, so I can't say whether the harness order holds for both models.

That is a fair hit and it matches the card, which says the model is not recommended for agentic coding. Even in Liquid's own table it trails Qwen3.5 9B on SWE-bench Verified, 43.40 against 51.20. The strengths they claim are tool calling and structured output, so I would keep coding on a larger model.

Thanks, the PMI result is a good check. The model card and release post don't say how the BFCL run orders its tools, so I can't rule out position effects. LFM3 writes tool calls as XML function calls rather than picking a letter, which may help, but nothing I have tests that. The card gives no Min-P guidance, only top-k and a repetition penalty.

A new model, if I had to pick one. Fresh context keeps the same weights and the same blind spots, so it can repeat a shared error. Cloudflare's harness post says models should be frequently interchanged and cross-tested, which targets this. The repo publishes no false-positive rates, so there is no direct comparison to cite.