Run MaziyarPanahi/gemma-3-27b-it-GGUF locally
MaziyarPanahi/gemma-3-27b-it-GGUF is a large instruction-tuned chat model with 27.01 billion parameters, built on the gemma3 architecture. It has been downloaded 104,537 times.
To run MaziyarPanahi/gemma-3-27b-it-GGUF locally at a 4,096-token context, its quantized versions need between 13.13 GB (Q2_K, lowest quality) and 30.08 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 23.99 GB. That means MaziyarPanahi/gemma-3-27b-it-GGUF fits entirely in the VRAM of a 16 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| Q2_K | 3.11 | Low | 9.78 GB | 2.54 GB | 13.13 GB | 5.1 t/s | Offload |
| Q3_K_S | 3.6 | Fair | 11.33 GB | 2.54 GB | 14.67 GB | 4.4 t/s | Offload |
| Q3_K_M | 3.98 | Fair | 12.51 GB | 2.54 GB | 15.86 GB | 4.0 t/s | Offload |
| Q3_K_L | 4.31 | Good | 13.54 GB | 2.54 GB | 16.89 GB | 3.7 t/s | Offload |
| Q4_K_S | 4.64 | Good | 14.6 GB | 2.54 GB | 17.94 GB | 3.4 t/s | Offload |
| Q4_K_M | 4.9 | Good | 15.41 GB | 2.54 GB | 18.75 GB | 3.2 t/s | Offload |
| Q5_K_S | 5.56 | Very good | 17.48 GB | 2.54 GB | 20.82 GB | 2.9 t/s | Offload |
| Q5_K_M | 5.71 | Very good | 17.95 GB | 2.54 GB | 21.29 GB | 2.8 t/s | Offload |
| Q6_K | 6.57 | Excellent | 20.64 GB | 2.54 GB | 23.99 GB | 2.4 t/s | Offload |
| Q8_0 | 8.5 | Excellent | 26.74 GB | 2.54 GB | 30.08 GB | — | Insufficient |
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 MaziyarPanahi/gemma-3-27b-it-GGUF?
You need about 15.86 GB of VRAM to run MaziyarPanahi/gemma-3-27b-it-GGUF entirely on the GPU using the Q3_K_M quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run MaziyarPanahi/gemma-3-27b-it-GGUF on an 8 GB GPU?
Partially. MaziyarPanahi/gemma-3-27b-it-GGUF only fits on an 8 GB GPU by offloading part of it to system RAM (with Q6_K), which runs but is slower.
Can I run MaziyarPanahi/gemma-3-27b-it-GGUF on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run MaziyarPanahi/gemma-3-27b-it-GGUF fully on the GPU using Q3_K_M (about 15.86 GB).
Can I run MaziyarPanahi/gemma-3-27b-it-GGUF on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run MaziyarPanahi/gemma-3-27b-it-GGUF fully on the GPU using Q6_K (about 23.99 GB).
What is the best quantization for MaziyarPanahi/gemma-3-27b-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.