Run unsloth/gemma-4-26B-A4B-it-qat-GGUF locally
unsloth/gemma-4-26B-A4B-it-qat-GGUF is a large instruction-tuned chat model with 25.23 billion parameters, built on the gemma4 architecture. It is released under the apache-2.0 license and has been downloaded 808,837 times.
To run unsloth/gemma-4-26B-A4B-it-qat-GGUF locally at a 4,096-token context, its quantized versions need between 3.46 GB (Q4_0, lowest quality) and 16.49 GB (Q4_K_XL, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is F32, needing about 5.36 GB. That means unsloth/gemma-4-26B-A4B-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 |
|---|---|---|---|---|---|---|---|
| Q4_0 | 0.08 | Very low | 0.23 GB | 2.42 GB | 3.46 GB | 1704.8 t/s | Fits in VRAM |
| GGUF | 0.08 | Very low | 0.23 GB | 2.42 GB | 3.46 GB | 1704.8 t/s | Fits in VRAM |
| Q8_0 | 0.15 | Very low | 0.43 GB | 2.42 GB | 3.65 GB | 930.1 t/s | Fits in VRAM |
| F16 | 0.65 | Very low | 1.91 GB | 2.42 GB | 5.13 GB | 209.7 t/s | Fits in VRAM |
| BF16 | 0.65 | Very low | 1.91 GB | 2.42 GB | 5.13 GB | 209.5 t/s | Fits in VRAM |
| F32 | 0.73 | Very low | 2.13 GB | 2.42 GB | 5.36 GB | 187.5 t/s | Fits in VRAM |
| Q4_K_XL | 4.52 | Good | 13.27 GB | 2.42 GB | 16.49 GB | 3.8 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 unsloth/gemma-4-26B-A4B-it-qat-GGUF?
You need about 5.36 GB of VRAM to run unsloth/gemma-4-26B-A4B-it-qat-GGUF entirely on the GPU using the F32 quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run unsloth/gemma-4-26B-A4B-it-qat-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-qat-GGUF fully on the GPU using F32 (about 5.36 GB).
Can I run unsloth/gemma-4-26B-A4B-it-qat-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-qat-GGUF fully on the GPU using F32 (about 5.36 GB).
Can I run unsloth/gemma-4-26B-A4B-it-qat-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-qat-GGUF fully on the GPU using Q4_K_XL (about 16.49 GB).
What is the best quantization for unsloth/gemma-4-26B-A4B-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.