Gemma-4-12B-StyleTune
Gryphe/Gemma-4-12B-StyleTuneGemma-4-12B-StyleTune at Q4_K_M is exactly 7,947,613,824 bytes (7.40 GiB / 7.95 GB) — an effective 4.904 bits per weight, not the nominal 4. Its KV cache at 32K is 2.47 GiB, not the 12.00 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| I1-IQ1_S | 3.09 GiB | 3,313,993,600 | 2.045 | — | mradermacher |
| I1-IQ1_M | 3.29 GiB | 3,533,150,080 | 2.180 | — | mradermacher |
| I1-IQ2_XXS | 3.63 GiB | 3,898,410,880 | 2.405 | — | mradermacher |
| I1-IQ2_XS | 3.93 GiB | 4,218,144,640 | 2.603 | — | mradermacher |
| I1-IQ2_S | 4.20 GiB | 4,512,135,040 | 2.784 | — | mradermacher |
| I1-IQ2_M | 4.47 GiB | 4,804,343,680 | 2.964 | — | mradermacher |
| I1-Q2_K_S | 4.50 GiB | 4,834,449,280 | 2.983 | — | mradermacher |
| Q2_K | 4.81 GiB | 5,160,449,664 | 3.184 | — | mradermacher |
| I1-Q2_K | 4.81 GiB | 5,160,449,920 | 3.184 | — | mradermacher |
| I1-IQ3_XXS | 4.92 GiB | 5,281,732,480 | 3.259 | — | mradermacher |
| I1-IQ3_XS | 5.31 GiB | 5,704,931,200 | 3.520 | — | mradermacher |
| Q3_K_S | 5.55 GiB | 5,960,767,104 | 3.678 | — | mradermacher |
| I1-Q3_K_S | 5.55 GiB | 5,960,767,360 | 3.678 | — | mradermacher |
| I1-IQ3_S | 5.55 GiB | 5,960,767,360 | 3.678 | — | mradermacher |
| I1-IQ3_M | 5.74 GiB | 6,166,529,920 | 3.805 | — | mradermacher |
| Q3_K_M | 6.07 GiB | 6,519,625,344 | 4.022 | — | mradermacher |
| I1-Q3_K_M | 6.07 GiB | 6,519,625,600 | 4.022 | — | mradermacher |
| Q3_K_L | 6.52 GiB | 6,998,857,344 | 4.318 | — | mradermacher |
| I1-Q3_K_L | 6.52 GiB | 6,998,857,600 | 4.318 | — | mradermacher |
| I1-IQ4_XS | 6.68 GiB | 7,170,029,440 | 4.424 | — | mradermacher |
| IQ4_XS | 6.73 GiB | 7,225,325,184 | 4.458 | — | mradermacher |
| I1-IQ4_NL | 7.02 GiB | 7,542,110,080 | 4.653 | — | mradermacher |
| I1-Q4_0 | 7.04 GiB | 7,564,228,480 | 4.667 | — | mradermacher |
| Q4_K_S | 7.07 GiB | 7,590,278,784 | 4.683 | — | mradermacher |
| I1-Q4_K_S | 7.07 GiB | 7,590,279,040 | 4.683 | — | mradermacher |
| Q4_K_M | 7.40 GiB | 7,947,613,824 | 4.904 | — | mradermacher |
| I1-Q4_K_M | 7.40 GiB | 7,947,614,080 | 4.904 | — | mradermacher |
| I1-Q4_1 | 7.72 GiB | 8,286,271,360 | 5.112 | — | mradermacher |
| Q5_K_S | 8.41 GiB | 9,030,432,384 | 5.572 | — | mradermacher |
| I1-Q5_K_S | 8.41 GiB | 9,030,432,640 | 5.572 | — | mradermacher |
| Q5_K_M | 8.60 GiB | 9,239,328,384 | 5.700 | — | mradermacher |
| I1-Q5_K_M | 8.60 GiB | 9,239,328,640 | 5.700 | — | mradermacher |
| Q6_K | 9.88 GiB | 10,611,775,104 | 6.547 | — | mradermacher |
| I1-Q6_K | 9.88 GiB | 10,611,775,360 | 6.547 | — | mradermacher |
| Q8_0 | 12.80 GiB | 13,739,193,984 | 8.477 | — | mradermacher |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.72 GiB | 1.50 GiB | 2.09× | 8 / 40 / 0 |
| 8,192 | 0.97 GiB | 3.00 GiB | 3.10× | 8 / 40 / 0 |
| 16,384 | 1.47 GiB | 6.00 GiB | 4.09× | 8 / 40 / 0 |
| 32,768 | 2.47 GiB | 12.00 GiB | 4.86× | 8 / 40 / 0 |
| 65,536 | 4.47 GiB | 24.00 GiB | 5.37× | 8 / 40 / 0 |
| 131,072 | 8.47 GiB | 48.00 GiB | 5.67× | 8 / 40 / 0 |
40 of 48 layers cache only a 1,024-token window rather than the full context, on a period of . Figures assume the default configuration; --swa-full disables the saving entirely.
Compare with
Will it run on your card?
Why other calculators give a different number
A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 6.79 GiB. The real file is 7.40 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 12.00 GiB at 32K context where the real figure is 2.47 GiB, because most of this model's layers cache a fixed window rather than the whole context.
Architecture
Questions people ask
- How much VRAM does Gemma-4-12B-StyleTune need?
- Q4_K_M is exactly 7,947,613,824 bytes (7.40 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
- How large is Gemma-4-12B-StyleTune's KV cache?
- 2.47 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
- Which quantization of Gemma-4-12B-StyleTune should I use?
- Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.