Run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF locally
yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF is a large language model with 11.91 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 355,871 times.
To run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF locally at a 4,096-token context, its quantized versions need between 3.27 GB (BF16, lowest quality) and 14.7 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is BF16, needing about 3.27 GB. That means yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-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 | 0.58 | Very low | 0.8 GB | 1.67 GB | 3.27 GB | 498.5 t/s | Fits in VRAM |
| F16 | 0.58 | Very low | 0.8 GB | 1.67 GB | 3.27 GB | 498.5 t/s | Fits in VRAM |
| Q3_K_M | 4.09 | Fair | 5.67 GB | 1.67 GB | 8.13 GB | 8.8 t/s | Offload |
| Q4_K_M | 4.96 | Good | 6.87 GB | 1.67 GB | 9.34 GB | 7.3 t/s | Offload |
| Q6_K | 6.57 | Excellent | 9.11 GB | 1.67 GB | 11.58 GB | 5.5 t/s | Offload |
| Q8_0 | 8.82 | Excellent | 12.23 GB | 1.67 GB | 14.7 GB | 4.1 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 yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF?
You need about 3.27 GB of VRAM to run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-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 yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF fully on the GPU using BF16 (about 3.27 GB).
Can I run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF fully on the GPU using Q8_0 (about 14.7 GB).
Can I run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF fully on the GPU using Q8_0 (about 14.7 GB).
What is the best quantization for yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-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.