Run paperscarecrow/Gemma-4-31B-it-abliterated locally
paperscarecrow/Gemma-4-31B-it-abliterated is a very large instruction-tuned chat model with 30.7 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 326,490 times.
To run paperscarecrow/Gemma-4-31B-it-abliterated locally at a 4,096-token context, its quantized versions need between 20.88 GB (Q4_K_M, lowest quality) and 60.67 GB (F16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q4_K_M, needing about 20.88 GB. That means paperscarecrow/Gemma-4-31B-it-abliterated fits entirely in the VRAM of a 24 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q4_K_M | 4.87 | Good | 17.4 GB | 2.67 GB | 20.88 GB | 2.9 t/s | Offload |
| Q8_0 | 8.51 | Excellent | 30.39 GB | 2.67 GB | 33.87 GB | — | Insufficient |
| F16 | 16.0 | Excellent | 57.2 GB | 2.67 GB | 60.67 GB | — | Insufficient |
KV cache estimated (architecture unavailable). Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run paperscarecrow/Gemma-4-31B-it-abliterated?
You need about 20.88 GB of VRAM to run paperscarecrow/Gemma-4-31B-it-abliterated entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run paperscarecrow/Gemma-4-31B-it-abliterated on an 8 GB GPU?
Partially. paperscarecrow/Gemma-4-31B-it-abliterated only fits on an 8 GB GPU by offloading part of it to system RAM (with Q4_K_M), which runs but is slower.
Can I run paperscarecrow/Gemma-4-31B-it-abliterated on a 16 GB GPU?
Partially. paperscarecrow/Gemma-4-31B-it-abliterated only fits on a 16 GB GPU by offloading part of it to system RAM (with Q8_0), which runs but is slower.
Can I run paperscarecrow/Gemma-4-31B-it-abliterated on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run paperscarecrow/Gemma-4-31B-it-abliterated fully on the GPU using Q4_K_M (about 20.88 GB).
What is the best quantization for paperscarecrow/Gemma-4-31B-it-abliterated?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.