Run unsloth/gemma-4-12b-it-GGUF locally
unsloth/gemma-4-12b-it-GGUF is a large instruction-tuned chat model with 11.91 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 1,390,513 times.
To run unsloth/gemma-4-12b-it-GGUF locally at a 4,096-token context, its quantized versions need between 2.66 GB (F32, lowest quality) and 25.63 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q3_K_M, needing about 7.77 GB. That means unsloth/gemma-4-12b-it-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| F32 | 0.14 | Very low | 0.2 GB | 1.67 GB | 2.66 GB | 2049.9 t/s | Fits in VRAM |
| GGUF | 0.31 | Very low | 0.43 GB | 1.67 GB | 2.9 GB | 923.4 t/s | Fits in VRAM |
| F16 | 0.7 | Very low | 0.97 GB | 1.67 GB | 3.43 GB | 414.3 t/s | Fits in VRAM |
| IQ2_M | 2.83 | Low | 3.92 GB | 1.67 GB | 6.39 GB | 101.9 t/s | Fits in VRAM |
| IQ3_XXS | 3.12 | Low | 4.32 GB | 1.67 GB | 6.79 GB | 92.6 t/s | Fits in VRAM |
| Q2_K_XL | 3.13 | Low | 4.34 GB | 1.67 GB | 6.81 GB | 92.1 t/s | Fits in VRAM |
| Q3_K_S | 3.45 | Fair | 4.78 GB | 1.67 GB | 7.25 GB | 83.6 t/s | Fits in VRAM |
| Q3_K_M | 3.83 | Fair | 5.3 GB | 1.67 GB | 7.77 GB | 75.4 t/s | Fits in VRAM |
| Q3_K_XL | 4.05 | Fair | 5.61 GB | 1.67 GB | 8.07 GB | 8.9 t/s | Offload |
| IQ4_XS | 4.28 | Good | 5.94 GB | 1.67 GB | 8.4 GB | 8.4 t/s | Offload |
| IQ4_NL | 4.51 | Good | 6.26 GB | 1.67 GB | 8.72 GB | 8.0 t/s | Offload |
| Q4_0 | 4.53 | Good | 6.28 GB | 1.67 GB | 8.74 GB | 8.0 t/s | Offload |
| Q4_K_S | 4.54 | Good | 6.3 GB | 1.67 GB | 8.77 GB | 7.9 t/s | Offload |
| Q4_K_M | 4.78 | Good | 6.63 GB | 1.67 GB | 9.1 GB | 7.5 t/s | Offload |
| Q4_K_XL | 4.95 | Good | 6.86 GB | 1.67 GB | 9.33 GB | 7.3 t/s | Offload |
| Q4_1 | 4.97 | Good | 6.89 GB | 1.67 GB | 9.35 GB | 7.3 t/s | Offload |
| Q5_K_S | 5.51 | Very good | 7.64 GB | 1.67 GB | 10.11 GB | 6.5 t/s | Offload |
| Q5_K_M | 5.65 | Very good | 7.84 GB | 1.67 GB | 10.3 GB | 6.4 t/s | Offload |
| Q5_K_XL | 5.78 | Very good | 8.01 GB | 1.67 GB | 10.48 GB | 6.2 t/s | Offload |
| Q6_K | 6.57 | Excellent | 9.11 GB | 1.67 GB | 11.58 GB | 5.5 t/s | Offload |
| Q6_K_XL | 7.18 | Excellent | 9.95 GB | 1.67 GB | 12.42 GB | 5.0 t/s | Offload |
| Q8_0 | 8.82 | Excellent | 12.23 GB | 1.67 GB | 14.7 GB | 4.1 t/s | Offload |
| Q8_K_XL | 9.16 | Excellent | 12.7 GB | 1.67 GB | 15.17 GB | 3.9 t/s | Offload |
| BF16 | 16.71 | Excellent | 23.16 GB | 1.67 GB | 25.63 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 unsloth/gemma-4-12b-it-GGUF?
You need about 3.43 GB of VRAM to run unsloth/gemma-4-12b-it-GGUF entirely on the GPU using the F16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/gemma-4-12b-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/gemma-4-12b-it-GGUF fully on the GPU using Q3_K_M (about 7.77 GB).
Can I run unsloth/gemma-4-12b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/gemma-4-12b-it-GGUF fully on the GPU using Q8_K_XL (about 15.17 GB).
Can I run unsloth/gemma-4-12b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/gemma-4-12b-it-GGUF fully on the GPU using Q8_K_XL (about 15.17 GB).
What is the best quantization for unsloth/gemma-4-12b-it-GGUF?
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.