Gemma 4 12B on Ollama: the GGUF is multimodal (CLIP crashes 0.30.2), it's a reasoning model, and Q4 has token glitches — fixes + Q4-vs-Q8 numbers

Pulled the new 
**Gemma 4 12B**
 via Ollama 0.30.2 (`hf.co/unsloth/gemma-4-12b-it-GGUF`) and ran it on a GTX 1080 Ti. Three things tripped me up before it worked, and the Q4-vs-Q8 comparison was more interesting than the speed. All measured, not estimated.


**Gotcha 1 — the GGUF is multimodal and the vision projector crashes the server.**
 First gen returned nothing:
```
error: Failed to load CLIP model from .../sha256-7d10888...
llama-server process has terminated: exit status 1
```
Gemma 4 12B-it ships a CLIP projector and Ollama 0.30.2 fails to load it, killing the model server. Text-only fix (no re-download):
```
ollama show --modelfile hf.co/unsloth/gemma-4-12b-it-GGUF:Q4_K_M > m.Modelfile
# delete the second FROM line (the mmproj/CLIP blob); keep only the text GGUF
ollama create gemma4-12b-text -f m.Modelfile
```


**Gotcha 2 — it's a reasoning model; the answer hides in 
`thinking`
.**
 `content` came back empty with eval_count=200 (it filled the reasoning channel and hit the cap). Fix: `"think": false` in /api/chat → clean answers.


**Gotcha 3 — Q4 has token glitches.**
 At Q4_K_M, prose had garbled chars (`self-さattention`, stray CJK codepoints mid-word). Code blocks were clean.


**Speed / VRAM:**
| | Q4_K_M | Q8_0 |
|---|---|---|
| Size / GPUs | 7 GB / 
**1 card**
 (2nd idle) | 12.7 GB / 
**2 cards**
 (~7 GB each) |
| Generation | 
**~28 tok/s**
 | ~19.5 tok/s (−30%) |
| Token glitches | yes | 
**gone**
 |


**The payoff:**
 I asked it niche domain questions (bioinformatics). At Q4 it nailed concepts and code but got one method (the HEIDI test in SMR/coloc) 
**confidently backwards**
 — said a small p-value means a single causal gene; it actually means the locus 
*fails*
 (heterogeneity). 
**Q8 fixed it**
 
*and*
 removed the token glitches. So less quantization bought correctness + clean output + (since 12.7 GB overflows one 11 GB card) it finally used the second GPU — at ~30% lower speed.


Takeaway: a 12B at Q4 fits one card and the 2nd GPU is dead weight; the 2nd card only earns its keep once the model overflows (Q8, or a bigger model). And for anything where details matter, the higher quant is worth the speed hit.


Anyone else seeing the CLIP-projector crash on multimodal GGUFs in 0.30.2, or a cleaner way to run them text-only?


Full writeup with the quality table: https://bric.pe.kr/blog/gemma-4-12b-gtx-1080-ti-q4-vs-q8