Run MaziyarPanahi/gemma-3-4b-it-GGUF locally
MaziyarPanahi/gemma-3-4b-it-GGUF is a mid-size instruction-tuned chat model with 3.88 billion parameters, built on the gemma3 architecture. It has been downloaded 178,053 times.
To run MaziyarPanahi/gemma-3-4b-it-GGUF locally at a 4,096-token context, its quantized versions need between 3.07 GB (Q2_K, lowest quality) and 8.7 GB (GGUF, 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 3.78 GB. That means MaziyarPanahi/gemma-3-4b-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 |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.56 | Fair | 1.61 GB | 0.66 GB | 3.07 GB | 248.4 t/s | Fits in VRAM |
| Q3_K_S | 3.99 | Fair | 1.8 GB | 0.66 GB | 3.27 GB | 221.7 t/s | Fits in VRAM |
| Q3_K_M | 4.33 | Good | 1.95 GB | 0.66 GB | 3.42 GB | 204.7 t/s | Fits in VRAM |
| Q3_K_L | 4.61 | Good | 2.08 GB | 0.66 GB | 3.55 GB | 192.1 t/s | Fits in VRAM |
| Q4_K_S | 4.9 | Good | 2.21 GB | 0.66 GB | 3.68 GB | 180.6 t/s | Fits in VRAM |
| Q4_K_M | 5.13 | Very good | 2.32 GB | 0.66 GB | 3.78 GB | 172.5 t/s | Fits in VRAM |
| Q5_K_S | 5.7 | Very good | 2.57 GB | 0.66 GB | 4.04 GB | 19.4 t/s | Offload |
| Q5_K_M | 5.83 | Very good | 2.64 GB | 0.66 GB | 4.1 GB | 19.0 t/s | Offload |
| Q6_K | 6.58 | Excellent | 2.97 GB | 0.66 GB | 4.44 GB | 16.8 t/s | Offload |
| Q8_0 | 8.52 | Excellent | 3.85 GB | 0.66 GB | 5.31 GB | 13.0 t/s | Offload |
| GGUF | 16.02 | Excellent | 7.23 GB | 0.66 GB | 8.7 GB | 6.9 t/s | Offload |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run MaziyarPanahi/gemma-3-4b-it-GGUF?
You need about 5.31 GB of VRAM to run MaziyarPanahi/gemma-3-4b-it-GGUF entirely on the GPU using the Q8_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/gemma-3-4b-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/gemma-3-4b-it-GGUF fully on the GPU using Q8_0 (about 5.31 GB).
Can I run MaziyarPanahi/gemma-3-4b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/gemma-3-4b-it-GGUF fully on the GPU using GGUF (about 8.7 GB).
Can I run MaziyarPanahi/gemma-3-4b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/gemma-3-4b-it-GGUF fully on the GPU using GGUF (about 8.7 GB).
What is the best quantization for MaziyarPanahi/gemma-3-4b-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.