Run lmstudio-community/gemma-4-E4B-it-GGUF locally
lmstudio-community/gemma-4-E4B-it-GGUF is a mid-size instruction-tuned chat model with 7.52 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 774,995 times.
To run lmstudio-community/gemma-4-E4B-it-GGUF locally at a 4,096-token context, its quantized versions need between 1.91 GB (BF16, lowest quality) and 8.47 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is Q6_K, needing about 6.78 GB. That means lmstudio-community/gemma-4-E4B-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 |
|---|---|---|---|---|---|---|---|
| BF16 | 1.06 | Very low | 0.92 GB | 0.19 GB | 1.91 GB | 433.2 t/s | Fits in VRAM |
| Q4_K_M | 5.68 | Very good | 4.97 GB | 0.19 GB | 5.96 GB | 80.5 t/s | Fits in VRAM |
| Q6_K | 6.62 | Excellent | 5.79 GB | 0.19 GB | 6.78 GB | 69.1 t/s | Fits in VRAM |
| Q8_0 | 8.55 | Excellent | 7.48 GB | 0.19 GB | 8.47 GB | 6.7 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 lmstudio-community/gemma-4-E4B-it-GGUF?
You need about 5.96 GB of VRAM to run lmstudio-community/gemma-4-E4B-it-GGUF entirely on the GPU using the Q4_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/gemma-4-E4B-it-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run lmstudio-community/gemma-4-E4B-it-GGUF fully on the GPU using Q6_K (about 6.78 GB).
Can I run lmstudio-community/gemma-4-E4B-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run lmstudio-community/gemma-4-E4B-it-GGUF fully on the GPU using Q8_0 (about 8.47 GB).
Can I run lmstudio-community/gemma-4-E4B-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/gemma-4-E4B-it-GGUF fully on the GPU using Q8_0 (about 8.47 GB).
What is the best quantization for lmstudio-community/gemma-4-E4B-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.