Run lmstudio-community/gemma-4-12B-it-QAT-GGUF locally
lmstudio-community/gemma-4-12B-it-QAT-GGUF is a large instruction-tuned chat model with 11.91 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 598,773 times.
To run lmstudio-community/gemma-4-12B-it-QAT-GGUF locally at a 4,096-token context, its quantized versions need between 2.63 GB (BF16, lowest quality) and 8.96 GB (Q4_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is BF16, needing about 2.63 GB. That means lmstudio-community/gemma-4-12B-it-QAT-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 | 0.12 | Very low | 0.16 GB | 1.67 GB | 2.63 GB | 2452.7 t/s | Fits in VRAM |
| Q4_0 | 4.69 | Good | 6.5 GB | 1.67 GB | 8.96 GB | 7.7 t/s | Offload |
KV cache estimated (architecture unavailable). Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run lmstudio-community/gemma-4-12B-it-QAT-GGUF?
You need about 2.63 GB of VRAM to run lmstudio-community/gemma-4-12B-it-QAT-GGUF entirely on the GPU using the BF16 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run lmstudio-community/gemma-4-12B-it-QAT-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run lmstudio-community/gemma-4-12B-it-QAT-GGUF fully on the GPU using BF16 (about 2.63 GB).
Can I run lmstudio-community/gemma-4-12B-it-QAT-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run lmstudio-community/gemma-4-12B-it-QAT-GGUF fully on the GPU using Q4_0 (about 8.96 GB).
Can I run lmstudio-community/gemma-4-12B-it-QAT-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run lmstudio-community/gemma-4-12B-it-QAT-GGUF fully on the GPU using Q4_0 (about 8.96 GB).
What is the best quantization for lmstudio-community/gemma-4-12B-it-QAT-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.