Run unsloth/gemma-4-31B-it-GGUF locally
unsloth/gemma-4-31B-it-GGUF 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 591,899 times.
To run unsloth/gemma-4-31B-it-GGUF locally at a 4,096-token context, its quantized versions need between 3.95 GB (GGUF, lowest quality) and 62.68 GB (BF16, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F32, needing about 5.62 GB. That means unsloth/gemma-4-31B-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 |
|---|---|---|---|---|---|---|---|
| GGUF | 0.13 | Very low | 0.48 GB | 2.67 GB | 3.95 GB | 834.5 t/s | Fits in VRAM |
| F16 | 0.56 | Very low | 2.01 GB | 2.67 GB | 5.48 GB | 199.4 t/s | Fits in VRAM |
| F32 | 0.6 | Very low | 2.14 GB | 2.67 GB | 5.62 GB | 186.5 t/s | Fits in VRAM |
| IQ2_XXS | 2.22 | Very low | 7.95 GB | 2.67 GB | 11.42 GB | 6.3 t/s | Offload |
| IQ2_M | 2.8 | Low | 10.01 GB | 2.67 GB | 13.49 GB | 5.0 t/s | Offload |
| Q2_K_XL | 3.07 | Low | 10.97 GB | 2.67 GB | 14.44 GB | 4.6 t/s | Offload |
| IQ3_XXS | 3.09 | Low | 11.02 GB | 2.67 GB | 14.5 GB | 4.5 t/s | Offload |
| Q3_K_S | 3.44 | Fair | 12.3 GB | 2.67 GB | 15.78 GB | 4.1 t/s | Offload |
| Q3_K_M | 3.84 | Fair | 13.72 GB | 2.67 GB | 17.2 GB | 3.6 t/s | Offload |
| Q3_K_XL | 4.01 | Fair | 14.32 GB | 2.67 GB | 17.8 GB | 3.5 t/s | Offload |
| IQ4_XS | 4.27 | Good | 15.25 GB | 2.67 GB | 18.72 GB | 3.3 t/s | Offload |
| IQ4_NL | 4.51 | Good | 16.1 GB | 2.67 GB | 19.57 GB | 3.1 t/s | Offload |
| Q4_0 | 4.52 | Good | 16.15 GB | 2.67 GB | 19.62 GB | 3.1 t/s | Offload |
| Q4_K_S | 4.53 | Good | 16.2 GB | 2.67 GB | 19.68 GB | 3.1 t/s | Offload |
| Q4_K_M | 4.78 | Good | 17.07 GB | 2.67 GB | 20.54 GB | 2.9 t/s | Offload |
| Q4_K_XL | 4.91 | Good | 17.53 GB | 2.67 GB | 21.0 GB | 2.9 t/s | Offload |
| Q4_1 | 4.98 | Good | 17.81 GB | 2.67 GB | 21.28 GB | 2.8 t/s | Offload |
| Q5_K_S | 5.51 | Very good | 19.67 GB | 2.67 GB | 23.15 GB | 2.5 t/s | Offload |
| Q5_K_M | 5.64 | Very good | 20.17 GB | 2.67 GB | 23.64 GB | 2.5 t/s | Offload |
| Q5_K_XL | 5.7 | Very good | 20.39 GB | 2.67 GB | 23.86 GB | 2.5 t/s | Offload |
| Q6_K | 6.57 | Excellent | 23.47 GB | 2.67 GB | 26.94 GB | — | Insufficient |
| Q6_K_XL | 7.17 | Excellent | 25.63 GB | 2.67 GB | 29.1 GB | — | Insufficient |
| Q8_0 | 8.64 | Excellent | 30.87 GB | 2.67 GB | 34.35 GB | — | Insufficient |
| Q8_K_XL | 9.13 | Excellent | 32.61 GB | 2.67 GB | 36.09 GB | — | Insufficient |
| BF16 | 16.57 | Excellent | 59.2 GB | 2.67 GB | 62.68 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-4-31B-it-GGUF?
You need about 5.62 GB of VRAM to run unsloth/gemma-4-31B-it-GGUF entirely on the GPU using the F32 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/gemma-4-31B-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/gemma-4-31B-it-GGUF fully on the GPU using F32 (about 5.62 GB).
Can I run unsloth/gemma-4-31B-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/gemma-4-31B-it-GGUF fully on the GPU using Q3_K_S (about 15.78 GB).
Can I run unsloth/gemma-4-31B-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/gemma-4-31B-it-GGUF fully on the GPU using Q5_K_XL (about 23.86 GB).
What is the best quantization for unsloth/gemma-4-31B-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.