Run unsloth/gemma-4-26B-A4B-it-GGUF locally
unsloth/gemma-4-26B-A4B-it-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 1,465,954 times.
To run unsloth/gemma-4-26B-A4B-it-GGUF locally at a 4,096-token context, its quantized versions need between 5.13 GB (F16, lowest quality) and 52.17 GB (BF16, 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-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 |
|---|---|---|---|---|---|---|---|
| F16 | 0.65 | Very low | 1.91 GB | 2.42 GB | 5.13 GB | 209.7 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 |
| IQ2_XXS | 3.15 | Low | 9.24 GB | 2.42 GB | 12.46 GB | 5.4 t/s | Offload |
| IQ2_M | 3.18 | Low | 9.33 GB | 2.42 GB | 12.55 GB | 5.4 t/s | Offload |
| Q2_K_XL | 3.34 | Fair | 9.82 GB | 2.42 GB | 13.05 GB | 5.1 t/s | Offload |
| IQ3_S | 3.58 | Fair | 10.51 GB | 2.42 GB | 13.74 GB | 4.8 t/s | Offload |
| IQ3_XXS | 3.62 | Fair | 10.63 GB | 2.42 GB | 13.86 GB | 4.7 t/s | Offload |
| Q3_K_M | 4.04 | Fair | 11.85 GB | 2.42 GB | 15.08 GB | 4.2 t/s | Offload |
| Q3_K_XL | 4.09 | Fair | 12.02 GB | 2.42 GB | 15.24 GB | 4.2 t/s | Offload |
| IQ4_XS | 4.31 | Good | 12.66 GB | 2.42 GB | 15.89 GB | 3.9 t/s | Offload |
| IQ4_NL | 4.32 | Good | 12.68 GB | 2.42 GB | 15.9 GB | 3.9 t/s | Offload |
| Q4_K_S | 5.23 | Very good | 15.36 GB | 2.42 GB | 18.58 GB | 3.3 t/s | Offload |
| Q4_K_M | 5.37 | Very good | 15.78 GB | 2.42 GB | 19.01 GB | 3.2 t/s | Offload |
| Q4_K_XL | 5.39 | Very good | 15.84 GB | 2.42 GB | 19.07 GB | 3.2 t/s | Offload |
| GGUF | 5.39 | Very good | 15.84 GB | 2.42 GB | 19.07 GB | 3.2 t/s | Offload |
| Q5_K_S | 5.98 | Very good | 17.56 GB | 2.42 GB | 20.78 GB | 2.8 t/s | Offload |
| Q5_K_M | 6.71 | Excellent | 19.7 GB | 2.42 GB | 22.92 GB | 2.5 t/s | Offload |
| Q5_K_XL | 6.73 | Excellent | 19.76 GB | 2.42 GB | 22.98 GB | 2.5 t/s | Offload |
| Q6_K | 7.35 | Excellent | 21.58 GB | 2.42 GB | 24.8 GB | — | Insufficient |
| Q6_K_XL | 7.39 | Excellent | 21.7 GB | 2.42 GB | 24.92 GB | — | Insufficient |
| Q8_0 | 8.66 | Excellent | 25.45 GB | 2.42 GB | 28.67 GB | — | Insufficient |
| Q8_K_XL | 8.76 | Excellent | 25.74 GB | 2.42 GB | 28.96 GB | — | Insufficient |
| BF16 | 16.66 | Excellent | 48.95 GB | 2.42 GB | 52.17 GB | — | Insufficient |
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-GGUF?
You need about 5.36 GB of VRAM to run unsloth/gemma-4-26B-A4B-it-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-GGUF on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-GGUF fully on the GPU using F32 (about 5.36 GB).
Can I run unsloth/gemma-4-26B-A4B-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-GGUF fully on the GPU using IQ4_NL (about 15.9 GB).
Can I run unsloth/gemma-4-26B-A4B-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run unsloth/gemma-4-26B-A4B-it-GGUF fully on the GPU using Q5_K_XL (about 22.98 GB).
What is the best quantization for unsloth/gemma-4-26B-A4B-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.