Gemma 4 12B quants compared
22 GGUF builds by real file size, probed from unsloth/gemma-4-12b-it-GGUF on Hugging Face (2026-09-02). 12B params.
Download Gemma 4 12B Q4_K_M (6.63 GB) — it fits 12 GB of memory with 16k context. With Ollama: ollama run gemma4:12b
Every Gemma 4 12B quant by real file size
| Quant | Weights | + KV (16k) | Total | Fits comfortably in | Quality |
|---|---|---|---|---|---|
| BF16 | 23 GB | 3.0 GB | 26.0 GB | 32 GB | Full precision (lossless) |
| F16 | 0.8 GB | 3.0 GB | 3.8 GB | 8 GB | Full precision (lossless) |
| Q8_K_XL | 12.7 GB | 3.0 GB | 15.7 GB | 24 GB | Near-lossless |
| Q8_0 | 12.23 GB | 3.0 GB | 15.2 GB | 24 GB | Near-lossless |
| Q6_K_XL | 9.95 GB | 3.0 GB | 12.9 GB | 16 GB | Excellent |
| Q6_K | 9.11 GB | 3.0 GB | 12.1 GB | 16 GB | Excellent |
| Q5_K_XL | 8.01 GB | 3.0 GB | 11.0 GB | 16 GB | Very high |
| Q5_K_M | 7.84 GB | 3.0 GB | 10.8 GB | 16 GB | Very high |
| Q5_K_S | 7.64 GB | 3.0 GB | 10.6 GB | 12 GB | Very high |
| Q4_K_M * | 6.63 GB | 3.0 GB | 9.6 GB | 12 GB | High — the default pick |
| Q4_1 | 6.89 GB | 3.0 GB | 9.9 GB | 12 GB | High |
| Q4_K_XL | 6.86 GB | 3.0 GB | 9.9 GB | 12 GB | High |
| Q4_K_S | 6.3 GB | 3.0 GB | 9.3 GB | 12 GB | High |
| Q4_0 | 6.28 GB | 3.0 GB | 9.3 GB | 12 GB | High |
| IQ4_NL | 6.26 GB | 3.0 GB | 9.3 GB | 12 GB | High |
| IQ4_XS | 5.94 GB | 3.0 GB | 8.9 GB | 12 GB | High |
| Q3_K_XL | 5.61 GB | 3.0 GB | 8.6 GB | 12 GB | Acceptable — visible loss |
| Q3_K_M | 5.3 GB | 3.0 GB | 8.3 GB | 12 GB | Acceptable — visible loss |
| Q3_K_S | 4.78 GB | 3.0 GB | 7.8 GB | 12 GB | Acceptable — visible loss |
| IQ3_XXS | 4.32 GB | 3.0 GB | 7.3 GB | 12 GB | Acceptable — visible loss |
| Q2_K_XL | 4.34 GB | 3.0 GB | 7.3 GB | 12 GB | Experimental — not ranked — never recommended |
| IQ2_M | 3.92 GB | 3.0 GB | 6.9 GB | 8 GB | Experimental — not ranked — never recommended |
* default pick. Weights = real GGUF file sizes from unsloth/gemma-4-12b-it-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.
Best Gemma 4 12B quant by memory
| Memory | Recommended quant | Total (16k ctx) |
|---|---|---|
| 8 GB | F16 | 3.8 GB |
| 32 GB | BF16 | 26.0 GB |
Why we don't rank Gemma 4 12B's 2-bit quants
Quants at 2 bits per weight or below (Q2_K, IQ2, IQ1, TQ1) cut file size by roughly half versus Q4, but the quality collapse is steep and non-linear: perplexity spikes, instruction-following degrades, and hallucinations rise. A model that answers faster but wrong is not a smaller model — it is a worse one. ModelFit lists these builds for completeness but never ranks or recommends them.
Frequently asked questions
What is the best quantization of Gemma 4 12B?
Q4_K_M is the default pick: 6.63 GB of weights, high — the default pick quality, fitting comfortably in 12 GB of memory (weights + 16k context KV-cache). Go Q6_K or Q8_0 if you have headroom.
How much memory does Gemma 4 12B need?
At Q4_K_M, Gemma 4 12B needs 6.63 GB for the weights plus ~3.0 GB of KV-cache at 16k context — about 9.6 GB total, so a 12 GB card or Mac (90% usable budget) runs it comfortably.
Should I use a Q2_K or IQ2 quant of Gemma 4 12B?
No. Gemma 4 12B at 2 bits per weight is a visibly worse model — quality collapse at that bitrate is steep, not gradual. If only a 2-bit build fits your memory, run a smaller model at Q4_K_M instead. ModelFit lists these builds but never recommends them.
Cite this page
ModelFit: Gemma 4 12B quantization comparison (real GGUF file sizes). https://modelfit.io/quant-compare/gemma4-12b/ (data probed 2026-09-02, CC BY 4.0).