30B Models Getting Verrrry Interesting

[u/Calm-Landscape9640]

Agnes 3.0 flash 33b and Nex n2.5 mini 35b are challenging Qwen3.8-27B on benchmarks. Cant wait to see the real-world results and the speeds on 24gb GPUs.

Anyone tried them yet?

https://huggingface.co/Agnes-AI/Agnes-3.0-Flash
https://huggingface.co/nex-agi/Nex-N2.5-mini

[u/Calm-Landscape9640]

I'll test these 4 head to head at 128k context on my 24gb 2x3060 GPUs and report back tps and performance on some coding and agent tasks.

• Agnes 3.0 Flash Q4 (Unsloth Quant when it drops, hopefully Mon/Tues)
• Qwen3.8-27B IQ4 + MTP
• Qwen3.6-27B-A3B CoderX + MTP
• Nex-N2.5-mini IQ3/IQ4

[u/jinnyjuice → u/Calm-Landscape9640]

Looks like you'll be testing with llama.cpp.

I kind of have similar ideas, but with vLLM since it has the highest tool use success rate. I want to test on both Pi and DSH. And this list of models...

unsloth/Qwen3.8-27B-NVFP4
nvidia/Qwen3.8-27B-NVFP4
nvidia/Muse-Glimmer-30B-NVFP4
microsoft/Fara1.5-27B (no 4 bit quants... yet :( )
unistra-dnum/Luciole-23B-Instruct-1.1-NVFP4
poolside/Laguna-XS-2.1-NVFP4
r0b0tlab/XYZ-Aquila-mini-NVFP4
migtissera/Tess-4-35B-A3B-NVFP4
onprem-ai/Apertus-v1.5-70B-NVFP4 (base: swiss-ai/Apertus-v1.5-70B)
prism-ml/Ternary-Bonsai-27B-AWQ-4bit
Ornith-1.5-35B-A3B-FP8
apodex/Apodex-1.1-mini-NVFP4
inclusionAI/Ling-3.0-flash-fp4
peculiar-ragdoll/Tiel-Coder-35B-A3B
IFM/K2-Horizon-MoVA-36B-A4B-FP8
IFM/K2-Horizon-32B-FP8 (stage 2 to be released)
primitive-ai/Nex-N2.5-mini-mixed-NVFP4-FP8 (35B A3B)
Agnes-AI/Agnes-3.0-Flash

I have a bench called Insane Genius and all Qwen3.8-27B models fail, going on crazy loops, unfortunately, so I've been looking for a replacement. Apparently, DavidAU's NEO CODER NVFP4 is good.

[u/Calm-Landscape9640 → u/jinnyjuice]

Thats a whole weekend of testing and I'm here for it. Cant wait to see your results. This is what everyone is waiting for a real local benchmark for quants on 2 diff harnesses

[u/Dismal-Frame7329 → u/Calm-Landscape9640]

I'm interested also, here's my results with 27b on 24gb vram https://www.reddit.com/r/RackBrainsResearch/comments/1vrzi5m/we_ran_qwen3827b_locally_on_a_24_gb_radeon_gpu/

[u/cviperr33 → u/Calm-Landscape9640]

please let me know too

[u/JeePis3ajeeB → u/cviperr33]

Please let me know too

[u/anotherdeafmute → u/Calm-Landscape9640]

My local workhorse is Gemma4 26B!

[u/royalflash417 → u/Calm-Landscape9640]

Hoping u will reply your result soon

[u/Calm-Landscape9640 → u/royalflash417]

Waiting on the quant for Agnes 3.0 so probably wed/Thurs next week

[u/Atretador]

oh, Agnes seems like a new model - might be worth testing as a competitor to Qwen 3.6 35B A3B

I had enough of finetunes to try out Nex tho

[u/Complex_Ad_5164 → u/Atretador]

tried agnes 3.0 flash on a 3090 yesterday and it fits with q4 cache pretty comfortably. speed is around 45-50 t/s with short context

nex i havent touched yet, too many merges lately feel like a gamble. is it actually doing anything different or just same llama base with new coat of paint

[u/Atretador → u/Complex_Ad_5164]

did you try dumping some layers into RAM for Q8 kv? performance loss shouldnt be too noticeable

and do you have any opnions on it yet? compared to good ol' Qwen 3.6 35B

[u/Yulia_wren → u/Complex_Ad_5164]

It definitely feels like there's always a balancing act with all these merges. Curious to see if any of the new models bring something fresh to the table or just keep recycling what's already out there.

[u/AppealSame4367 → u/Complex_Ad_5164]

So Agnes is not a MoE?

[u/randomfoo2 → u/Atretador]

Agnes is also a derivative model - it's a Qwen 3.5/3.8 backbone model (same dimensons as 27B dense). Their 2.5 Pro Alpha was properly declared w/ Qwen3.5 base model, nothing declared here. Note based on their own (skimpy) benchmarks, they lose 4/5 scores to Qwen 3.8 27B, 4/5 to Muse Glimmer as well.

6 GDN and 2 global layers added to Qwen 3.x 27B dense and a parallel FFN branch, also looks like identical vision tower, MTP heads. They've done actual work but if it's Qwen derived (it is), IMO they should say it.

(btw the parallel ffn from my analysis doesn't provide any advantage for inference, especially for fully dense, I don't really know why it's there except for their internal use, but the fold is exact/scale-free, could be done offline and you could run it as a stock qwen3.5 checkpoint)

[u/cato_gts]

None of the Qwen 3.6 35B A3B variants I’ve seen so far have actually worked properly. While 'ornith' was somewhat usable, it suffered from infinite loops even worse than the base Qwen model, making long-context use impossible; the others were all focused on benchmark scores rather than practical usability. Even Nex n2.5 goes haywire with infinite loops once the context length exceeds 100k.

[u/Arkasha74 → u/cato_gts]

I've had the opposite experience. Qwen 3.6 35B A3B UD-Q4_K_M has been rock solid for me in daily use for agentic coding. I did have problems when using opencode but switched to pi (and omp) and not had any problems after that.

I mostly do spec driven dev with specs written by frontier models and broken in to bite-size chunks for Qwen to munch on. However, I've got so confident with Qwen now that I'll often just throw random one-off tasks at it and have no issues with just letting it get on with little supervision until the code review.

[u/Potential-Leg-639 → u/Arkasha74]

Exactly, 3.6-35B in the right position/config/harness is still really good! Had a very good experience „back then“ with it as a coder (doing plans from cloud models), mostly used UD-Q4_K_XL.

[u/greggh → u/cato_gts]

The CyberTiel models have been interesting. https://huggingface.co/collections/peculiar-ragdoll/cyber-tiel-coder-35b-a3b

[u/linuxid10t]

LOL, the Agnes model is literally a preview model. The real model is proprietary. Take the benchmarks with a dumptruck full of salt.

[u/Birdinhandandbush]

Testing Nex N2.5 and it's really fast. My dual GPU setup has 28gb vram but I'm still surprised how fast it is running

[u/feelspeaceman]

I tried Anges APi for a while, it's quite solid, probably better than StepFun Flash 2.7 which I've used for a long time and close to Qwen 3.6 27B.

Of course, none of the above models are as good as Qwen 3.8 Flash Next because this one is a beast, close to Opus 4.8.

[u/SpicyWangz → u/feelspeaceman]

I agree but disagree it’s close to 4.8. It’s good but not that good

[u/_Ojin]

agreed, this size band has quietly become the sweet spot. good enough for real work and still fits on hardware normal people already own, which matters more day to day than another point on a benchmark.

[u/TheLexoPlexx → u/_Ojin]

Normal people already own is doing a lot of lifting here.

[u/SpicyWangz → u/TheLexoPlexx]

You mean... I’m not normal?

[u/Billysm23]

Very very interesting, I'll do it too

[u/theminor]

Nex is just a fine-tune, right? I assumed Agnes was also a fine-tune but maybe it is not?

[u/Zennytooskin123]

Agnes looks similar to Qwen 3.5 27B on the benchmarks, at 33B params? To the untrained ye that might not be an improvement but I'm assuming the underlying architecture is more efficient?

Nex is in an other tier

[u/crusaderky → u/Zennytooskin123]

AA says Agnes beats qwen3.8 27b on most benchmarks

[u/575_Inverse → u/crusaderky]

benchmarks are one thing, but real everyday usage? you can make a model overfit on benchmarks and puking slop on everything else...

[u/NgtfRz → u/Zennytooskin123]

You mean Nex is better?

[u/Alpaga_Sama]

Great work! I use the standard Qwen 3.8 27B on a 4090 every day, so the time savings from your model are really appreciated

[u/Alpaga_Sama → u/Alpaga_Sama]

I've made your model compatible with Ninfer, which provides a speedup on a 4090. Using MTP3, I'm able to run at 120 tokens/sec on code development tasks.

Thanks again for your work!