Run MaziyarPanahi/gemma-3-1b-it-GGUF locally
MaziyarPanahi/gemma-3-1b-it-GGUF is a compact instruction-tuned chat model with 1.0 billion parameters, built on the gemma3 architecture. It has been downloaded 160,557 times.
To run MaziyarPanahi/gemma-3-1b-it-GGUF locally at a 4,096-token context, its quantized versions need between 1.56 GB (Q3_K_S, lowest quality) and 2.78 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is GGUF, needing about 2.78 GB. That means MaziyarPanahi/gemma-3-1b-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 |
|---|---|---|---|---|---|---|---|
| Q3_K_S | 5.51 | Very good | 0.64 GB | 0.11 GB | 1.56 GB | 623.5 t/s | Fits in VRAM |
| Q2_K | 5.52 | Very good | 0.64 GB | 0.11 GB | 1.56 GB | 622.6 t/s | Fits in VRAM |
| Q3_K_M | 5.78 | Very good | 0.67 GB | 0.11 GB | 1.59 GB | 594.5 t/s | Fits in VRAM |
| Q3_K_L | 6.01 | Very good | 0.7 GB | 0.11 GB | 1.61 GB | 571.5 t/s | Fits in VRAM |
| Q4_K_S | 6.25 | Very good | 0.73 GB | 0.11 GB | 1.64 GB | 549.9 t/s | Fits in VRAM |
| Q4_K_M | 6.45 | Very good | 0.75 GB | 0.11 GB | 1.66 GB | 532.8 t/s | Fits in VRAM |
| Q5_K_S | 6.69 | Excellent | 0.78 GB | 0.11 GB | 1.69 GB | 513.5 t/s | Fits in VRAM |
| Q5_K_M | 6.81 | Excellent | 0.79 GB | 0.11 GB | 1.71 GB | 504.5 t/s | Fits in VRAM |
| Q6_K | 8.09 | Excellent | 0.94 GB | 0.11 GB | 1.86 GB | 424.5 t/s | Fits in VRAM |
| Q8_0 | 8.56 | Excellent | 1.0 GB | 0.11 GB | 1.91 GB | 401.7 t/s | Fits in VRAM |
| GGUF | 16.05 | Excellent | 1.87 GB | 0.11 GB | 2.78 GB | 214.0 t/s | Fits in VRAM |
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-3-1b-it-GGUF?
You need about 2.78 GB of VRAM to run MaziyarPanahi/gemma-3-1b-it-GGUF entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/gemma-3-1b-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run MaziyarPanahi/gemma-3-1b-it-GGUF fully on the GPU using GGUF (about 2.78 GB).
Can I run MaziyarPanahi/gemma-3-1b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/gemma-3-1b-it-GGUF fully on the GPU using GGUF (about 2.78 GB).
Can I run MaziyarPanahi/gemma-3-1b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/gemma-3-1b-it-GGUF fully on the GPU using GGUF (about 2.78 GB).
What is the best quantization for MaziyarPanahi/gemma-3-1b-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.