gemma-4-12B-it-abliterated-uncensored
OpenYourMind/gemma-4-12B-it-abliterated-uncensoredgemma-4-12B-it-abliterated-uncensored at Q4_K_M is exactly 7,381,382,464 bytes (6.87 GiB / 7.38 GB) — an effective 4.938 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 | 2.78 GiB | 2,983,692,256 | 1.996 | — | mradermacher |
| I1-IQ1_M | 2.98 GiB | 3,202,848,736 | 2.142 | — | mradermacher |
| I1-IQ2_XXS | 3.32 GiB | 3,568,109,536 | 2.387 | — | mradermacher |
| I1-IQ2_XS | 3.62 GiB | 3,887,843,296 | 2.601 | — | mradermacher |
| I1-IQ2_S | 3.80 GiB | 4,079,597,536 | 2.729 | — | mradermacher |
| I1-IQ2_M | 4.07 GiB | 4,371,806,176 | 2.924 | — | mradermacher |
| I1-Q2_K_S | 4.19 GiB | 4,504,147,936 | 3.013 | — | mradermacher |
| Q2_K | 4.50 GiB | 4,830,148,288 | 3.231 | — | mradermacher |
| I1-Q2_K | 4.50 GiB | 4,830,148,576 | 3.231 | — | mradermacher |
| I1-IQ3_XXS | 4.52 GiB | 4,849,194,976 | 3.244 | — | mradermacher |
| I1-IQ3_XS | 4.91 GiB | 5,272,393,696 | 3.527 | — | mradermacher |
| Q3_K_S | 5.15 GiB | 5,528,229,568 | 3.698 | — | mradermacher |
| I1-IQ3_S | 5.15 GiB | 5,528,229,856 | 3.698 | — | mradermacher |
| I1-Q3_K_S | 5.15 GiB | 5,528,229,856 | 3.698 | — | mradermacher |
| I1-IQ3_M | 5.34 GiB | 5,733,992,416 | 3.836 | — | mradermacher |
| Q3_K_M | 5.67 GiB | 6,087,087,808 | 4.072 | — | mradermacher |
| I1-Q3_K_M | 5.67 GiB | 6,087,088,096 | 4.072 | — | mradermacher |
| Q3_K_L | 6.12 GiB | 6,566,319,424 | 4.392 | — | DuoNeural |
| Q3_K_L | 6.12 GiB | 6,566,319,808 | 4.392 | — | mradermacher |
| I1-Q3_K_L | 6.12 GiB | 6,566,320,096 | 4.392 | — | mradermacher |
| I1-IQ4_XS | 6.18 GiB | 6,635,255,776 | 4.438 | — | mradermacher |
| IQ4_XS | 6.23 GiB | 6,690,551,488 | 4.475 | — | mradermacher |
| I1-IQ4_NL | 6.50 GiB | 6,975,879,136 | 4.666 | — | mradermacher |
| I1-Q4_0 | 6.52 GiB | 6,997,997,536 | 4.681 | — | mradermacher |
| Q4_K_S | 6.54 GiB | 7,024,047,808 | 4.699 | — | mradermacher |
| I1-Q4_K_S | 6.54 GiB | 7,024,048,096 | 4.699 | — | mradermacher |
| Q4_K_M | 6.87 GiB | 7,381,382,464 | 4.938 | — | DuoNeural |
| Q4_K_M | 6.87 GiB | 7,381,382,848 | 4.938 | — | mradermacher |
| I1-Q4_K_M | 6.87 GiB | 7,381,383,136 | 4.938 | — | mradermacher |
| I1-Q4_1 | 7.13 GiB | 7,657,125,856 | 5.122 | — | mradermacher |
| Q5_K_S | 7.77 GiB | 8,338,372,288 | 5.578 | — | mradermacher |
| I1-Q5_K_S | 7.77 GiB | 8,338,372,576 | 5.578 | — | mradermacher |
| Q5_K_M | 7.96 GiB | 8,547,267,904 | 5.717 | — | DuoNeural |
| Q5_K_M | 7.96 GiB | 8,547,268,288 | 5.717 | — | mradermacher |
| I1-Q5_K_M | 7.96 GiB | 8,547,268,576 | 5.717 | — | mradermacher |
| Q6_K | 9.11 GiB | 9,786,021,568 | 6.546 | — | mradermacher |
| I1-Q6_K | 9.11 GiB | 9,786,021,856 | 6.546 | — | mradermacher |
| Q8_0 | 11.80 GiB | 12,669,646,144 | 8.475 | — | DuoNeural |
| Q8_0 | 11.80 GiB | 12,669,646,528 | 8.475 | — | 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 6.27 GiB. The real file is 6.87 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-it-abliterated-uncensored need?
- Q4_K_M is exactly 7,381,382,464 bytes (6.87 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-it-abliterated-uncensored'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-it-abliterated-uncensored 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.