gemma-2-9b-it GGUF size and VRAM requirements
The **gemma-2-9b-it** model is a 9.24 billion parameter AI model from the Gemma family, licensed under the gemma license. It is designed for text generation and dialogue tasks, with a focus on instruction-following capabilities. The model does not support System prompt inputs.
To run bartowski/gemma-2-9b-it-GGUF locally at a 4,096-token context, its quantized versions need between 4.81 GB (IQ2_XS, lowest quality) and 36.38 GB (F32, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q5_K_S, needing about 7.99 GB. That means bartowski/gemma-2-9b-it-GGUF fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| IQ2_XS | 2.66 | Low | 2.86 GB | 1.15 GB | 4.81 GB | 140.0 t/s | Fits in VRAM |
| IQ2_S | 2.78 | Low | 2.99 GB | 1.15 GB | 4.94 GB | 133.7 t/s | Fits in VRAM |
| IQ2_M | 2.97 | Low | 3.2 GB | 1.15 GB | 5.15 GB | 125.0 t/s | Fits in VRAM |
| IQ3_XXS | 3.29 | Low | 3.54 GB | 1.15 GB | 5.48 GB | 113.1 t/s | Fits in VRAM |
| Q2_K | 3.29 | Low | 3.54 GB | 1.15 GB | 5.49 GB | 112.9 t/s | Fits in VRAM |
| Q2_K_L | 3.49 | Fair | 3.75 GB | 1.15 GB | 5.7 GB | 106.6 t/s | Fits in VRAM |
| IQ3_XS | 3.59 | Fair | 3.86 GB | 1.15 GB | 5.81 GB | 103.6 t/s | Fits in VRAM |
| Q3_K_S | 3.75 | Fair | 4.04 GB | 1.15 GB | 5.99 GB | 99.0 t/s | Fits in VRAM |
| IQ3_M | 3.89 | Fair | 4.19 GB | 1.15 GB | 6.13 GB | 95.6 t/s | Fits in VRAM |
| Q3_K_M | 4.12 | Fair | 4.43 GB | 1.15 GB | 6.38 GB | 90.2 t/s | Fits in VRAM |
| IQ4_XS | 4.49 | Good | 4.83 GB | 1.15 GB | 6.78 GB | 82.9 t/s | Fits in VRAM |
| Q3_K_XL | 4.64 | Good | 4.99 GB | 1.15 GB | 6.94 GB | 80.2 t/s | Fits in VRAM |
| Q4_K_S | 4.74 | Good | 5.1 GB | 1.15 GB | 7.05 GB | 78.4 t/s | Fits in VRAM |
| Q4_K_L | 5.18 | Very good | 5.57 GB | 1.15 GB | 7.52 GB | 71.8 t/s | Fits in VRAM |
| Q5_K_S | 5.61 | Very good | 6.04 GB | 1.15 GB | 7.99 GB | 66.2 t/s | Fits in VRAM |
| Q5_K_M | 5.75 | Very good | 6.19 GB | 1.15 GB | 8.14 GB | 8.1 t/s | Offload |
| Q5_K_L | 5.95 | Very good | 6.4 GB | 1.15 GB | 8.35 GB | 7.8 t/s | Offload |
| Q6_K_L | 6.76 | Excellent | 7.27 GB | 1.15 GB | 9.22 GB | 6.9 t/s | Offload |
| Q3_K_L | 9.08 | Excellent | 9.77 GB | 1.15 GB | 11.72 GB | 5.1 t/s | Offload |
| Q8_0_L | 9.25 | Excellent | 9.95 GB | 1.15 GB | 11.9 GB | 5.0 t/s | Offload |
| Q4_K_M | 10.91 | Excellent | 11.74 GB | 1.15 GB | 13.69 GB | 4.3 t/s | Offload |
| Q8_0 | 17.76 | Excellent | 19.11 GB | 1.15 GB | 21.05 GB | 2.6 t/s | Offload |
| Q6_K | 22.43 | Excellent | 24.13 GB | 1.15 GB | 26.08 GB | — | Insufficient |
| F32 | 32.01 | Excellent | 34.43 GB | 1.15 GB | 36.38 GB | — | Insufficient |
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 bartowski/gemma-2-9b-it-GGUF?
You need about 5.99 GB of VRAM to run bartowski/gemma-2-9b-it-GGUF entirely on the GPU using the Q3_K_S quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run bartowski/gemma-2-9b-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/gemma-2-9b-it-GGUF fully on the GPU using Q5_K_S (about 7.99 GB).
Can I run bartowski/gemma-2-9b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/gemma-2-9b-it-GGUF fully on the GPU using Q4_K_M (about 13.69 GB).
Can I run bartowski/gemma-2-9b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/gemma-2-9b-it-GGUF fully on the GPU using Q8_0 (about 21.05 GB).
What is the best quantization for bartowski/gemma-2-9b-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.