Run bartowski/gemma-2-2b-it-GGUF locally
The **gemma-2-2b-it** model is part of the Gemma series developed by Google, featuring 2.61 billion parameters and released under the Gemma license. It is designed for instruction-following tasks, optimized for efficiency and performance in general-purpose text generation and conversational applications. The model is derived from the original Gemma-2-2b-it base available on Hugging Face.
To run bartowski/gemma-2-2b-it-GGUF locally at a 4,096-token context, its quantized versions need between 2.55 GB (IQ3_M, lowest quality) and 11.0 GB (F32, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q8_0, needing about 3.85 GB. That means bartowski/gemma-2-2b-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 |
|---|---|---|---|---|---|---|---|
| IQ3_M | 4.26 | Good | 1.3 GB | 0.46 GB | 2.55 GB | 308.2 t/s | Fits in VRAM |
| Q3_K_L | 4.74 | Good | 1.44 GB | 0.46 GB | 2.7 GB | 277.0 t/s | Fits in VRAM |
| IQ4_XS | 4.79 | Good | 1.46 GB | 0.46 GB | 2.72 GB | 274.2 t/s | Fits in VRAM |
| Q4_K_S | 5.01 | Very good | 1.53 GB | 0.46 GB | 2.78 GB | 262.1 t/s | Fits in VRAM |
| Q4_K_M | 5.23 | Very good | 1.59 GB | 0.46 GB | 2.85 GB | 251.4 t/s | Fits in VRAM |
| Q5_K_S | 5.76 | Very good | 1.75 GB | 0.46 GB | 3.01 GB | 228.1 t/s | Fits in VRAM |
| Q5_K_M | 5.89 | Very good | 1.79 GB | 0.46 GB | 3.05 GB | 223.3 t/s | Fits in VRAM |
| Q6_K | 6.58 | Excellent | 2.0 GB | 0.46 GB | 3.26 GB | 199.6 t/s | Fits in VRAM |
| Q6_K_L | 7.02 | Excellent | 2.14 GB | 0.46 GB | 3.39 GB | 187.2 t/s | Fits in VRAM |
| Q8_0 | 8.52 | Excellent | 2.59 GB | 0.46 GB | 3.85 GB | 154.2 t/s | Fits in VRAM |
| F32 | 32.02 | Excellent | 9.74 GB | 0.46 GB | 11.0 GB | 5.1 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 bartowski/gemma-2-2b-it-GGUF?
You need about 3.85 GB of VRAM to run bartowski/gemma-2-2b-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 bartowski/gemma-2-2b-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run bartowski/gemma-2-2b-it-GGUF fully on the GPU using Q8_0 (about 3.85 GB).
Can I run bartowski/gemma-2-2b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run bartowski/gemma-2-2b-it-GGUF fully on the GPU using F32 (about 11.0 GB).
Can I run bartowski/gemma-2-2b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run bartowski/gemma-2-2b-it-GGUF fully on the GPU using F32 (about 11.0 GB).
What is the best quantization for bartowski/gemma-2-2b-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.