Run google/gemma-4-E2B-it-qat-q4_0-gguf locally
google/gemma-4-E2B-it-qat-q4_0-gguf is a mid-size instruction-tuned chat model with 4.63 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 320,941 times.
To run google/gemma-4-E2B-it-qat-q4_0-gguf locally at a 4,096-token context, its quantized versions need between 1.91 GB (GGUF, lowest quality) and 4.11 GB (Q4_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q4_0, needing about 4.11 GB. That means google/gemma-4-E2B-it-qat-q4_0-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 | 1.71 | Very low | 0.92 GB | 0.19 GB | 1.91 GB | 435.2 t/s | Fits in VRAM |
| Q4_0 | 5.79 | Very good | 3.12 GB | 0.19 GB | 4.11 GB | 128.2 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 google/gemma-4-E2B-it-qat-q4_0-gguf?
You need about 4.11 GB of VRAM to run google/gemma-4-E2B-it-qat-q4_0-gguf entirely on the GPU using the Q4_0 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run google/gemma-4-E2B-it-qat-q4_0-gguf on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run google/gemma-4-E2B-it-qat-q4_0-gguf fully on the GPU using Q4_0 (about 4.11 GB).
Can I run google/gemma-4-E2B-it-qat-q4_0-gguf on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run google/gemma-4-E2B-it-qat-q4_0-gguf fully on the GPU using Q4_0 (about 4.11 GB).
Can I run google/gemma-4-E2B-it-qat-q4_0-gguf on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run google/gemma-4-E2B-it-qat-q4_0-gguf fully on the GPU using Q4_0 (about 4.11 GB).
What is the best quantization for google/gemma-4-E2B-it-qat-q4_0-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.