gemma-4-E4B-it-abliterated
WWTCyberLab/gemma-4-E4B-it-abliteratedgemma-4-E4B-it-abliterated at I1-IQ1_S is exactly 3,289,758,368 bytes (3.06 GiB / 3.29 GB) — an effective 3.291 bits per weight, not the nominal 1. Its KV cache at 32K is 0.51 GiB, not the 2.63 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| I1-IQ1_S | 3.06 GiB | 3,289,758,368 | 3.291 | — | mradermacher |
| I1-IQ1_M | 3.14 GiB | 3,372,272,288 | 3.374 | — | mradermacher |
| I1-IQ2_XXS | 3.27 GiB | 3,509,795,488 | 3.511 | — | mradermacher |
| I1-IQ2_XS | 3.38 GiB | 3,627,842,208 | 3.630 | — | mradermacher |
| I1-IQ2_S | 3.42 GiB | 3,677,280,928 | 3.679 | — | mradermacher |
| I1-IQ2_M | 3.53 GiB | 3,787,299,488 | 3.789 | — | mradermacher |
| I1-IQ3_XXS | 3.71 GiB | 3,982,842,528 | 3.985 | — | mradermacher |
| I1-Q2_K_S | 3.98 GiB | 4,269,591,200 | 4.272 | — | mradermacher |
| I1-Q2_K | 4.10 GiB | 4,401,316,544 | 4.403 | — | mradermacher |
| I1-IQ3_XS | 4.23 GiB | 4,545,886,880 | 4.548 | — | mradermacher |
| I1-Q3_K_S | 4.33 GiB | 4,654,633,664 | 4.657 | — | mradermacher |
| I1-IQ3_S | 4.34 GiB | 4,663,163,584 | 4.665 | — | mradermacher |
| I1-IQ3_M | 4.39 GiB | 4,714,691,264 | 4.717 | — | mradermacher |
| I1-Q3_K_M | 4.52 GiB | 4,850,391,744 | 4.853 | — | mradermacher |
| I1-Q3_K_L | 4.68 GiB | 5,021,276,864 | 5.024 | — | mradermacher |
| I1-IQ4_XS | 4.72 GiB | 5,070,951,104 | 5.073 | — | mradermacher |
| I1-IQ4_NL | 4.84 GiB | 5,193,953,984 | 5.197 | — | mradermacher |
| I1-Q4_0 | 4.84 GiB | 5,194,117,824 | 5.197 | — | mradermacher |
| I1-Q4_K_S | 4.85 GiB | 5,202,965,184 | 5.205 | — | mradermacher |
| I1-Q4_K_M | 4.97 GiB | 5,335,286,464 | 5.338 | — | mradermacher |
| I1-Q4_1 | 5.06 GiB | 5,435,945,664 | 5.439 | — | mradermacher |
| I1-Q5_K_S | 5.30 GiB | 5,685,965,504 | 5.689 | — | mradermacher |
| I1-Q5_K_M | 5.37 GiB | 5,762,908,864 | 5.766 | — | mradermacher |
| I1-Q6_K | 5.79 GiB | 6,217,257,664 | 6.220 | — | mradermacher |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.12 GiB | 0.33 GiB | 2.67× | 7 / 35 / 0 |
| 8,192 | 0.18 GiB | 0.66 GiB | 3.69× | 7 / 35 / 0 |
| 16,384 | 0.29 GiB | 1.31 GiB | 4.57× | 7 / 35 / 0 |
| 32,768 | 0.51 GiB | 2.63 GiB | 5.19× | 7 / 35 / 0 |
| 65,536 | 0.94 GiB | 5.25 GiB | 5.57× | 7 / 35 / 0 |
| 131,072 | 1.82 GiB | 10.50 GiB | 5.77× | 7 / 35 / 0 |
35 of 42 layers cache only a 512-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 I1-IQ1_S at roughly 4.19 GiB. The real file is 3.06 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 2.63 GiB at 32K context where the real figure is 0.51 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-E4B-it-abliterated need?
- I1-IQ1_S is exactly 3,289,758,368 bytes (3.06 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-E4B-it-abliterated's KV cache?
- 0.51 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-E4B-it-abliterated 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.