gemma-3-4b-it-abliterated
mlabonne/gemma-3-4b-it-abliteratedgemma-3-4b-it-abliterated at Q4_K_M is exactly 2,489,894,304 bytes (2.32 GiB / 2.49 GB) — an effective 4.632 bits per weight, not the nominal 4. Its KV cache at 32K is 0.79 GiB, not the 4.25 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| IQ2_M | 1.43 GiB | 1,537,982,624 | 2.861 | — | bartowski |
| IQ3_XXS | 1.57 GiB | 1,689,452,704 | 3.143 | — | bartowski |
| Q2_K | 1.61 GiB | 1,729,164,704 | 3.217 | — | bartowski |
| Q2_K | 1.61 GiB | 1,729,164,864 | 3.217 | — | mradermacher |
| IQ3_XS | 1.74 GiB | 1,863,390,624 | 3.467 | — | bartowski |
| Q2_K_L | 1.76 GiB | 1,891,733,664 | 3.519 | — | bartowski |
| Q3_K_S | 1.80 GiB | 1,937,364,384 | 3.604 | — | bartowski |
| Q3_K_S | 1.80 GiB | 1,937,364,544 | 3.604 | — | mradermacher |
| IQ3_M | 1.85 GiB | 1,986,803,104 | 3.696 | — | bartowski |
| Q3_K_M | 1.95 GiB | 2,098,460,064 | 3.904 | — | bartowski |
| Q3_K_M | 1.95 GiB | 2,098,460,224 | 3.904 | — | mradermacher |
| Q3_K_L | 2.08 GiB | 2,236,085,664 | 4.160 | — | bartowski |
| Q3_K_L | 2.08 GiB | 2,236,085,824 | 4.160 | — | mradermacher |
| IQ4_XS | 2.11 GiB | 2,263,242,144 | 4.211 | — | bartowski |
| IQ4_XS | 2.12 GiB | 2,279,626,304 | 4.241 | — | mradermacher |
| IQ4_NL | 2.20 GiB | 2,363,512,224 | 4.397 | — | bartowski |
| Q4_0 | 2.21 GiB | 2,370,065,824 | 4.409 | — | bartowski |
| Q4_K_S | 2.21 GiB | 2,377,930,144 | 4.424 | — | bartowski |
| Q4_K_S | 2.21 GiB | 2,377,930,304 | 4.424 | — | mradermacher |
| Q4_K_M | 2.32 GiB | 2,489,894,304 | 4.632 | — | bartowski |
| Q4_K_M | 2.32 GiB | 2,489,894,464 | 4.632 | — | mradermacher |
| Q4_1 | 2.39 GiB | 2,564,052,384 | 4.770 | — | bartowski |
| Q4_K_L | 2.47 GiB | 2,652,463,264 | 4.935 | — | bartowski |
| Q5_K_S | 2.57 GiB | 2,764,592,544 | 5.143 | — | bartowski |
| Q5_K_S | 2.57 GiB | 2,764,592,704 | 5.143 | — | mradermacher |
| Q5_K_M | 2.64 GiB | 2,829,698,464 | 5.264 | — | bartowski |
| Q5_K_M | 2.64 GiB | 2,829,698,624 | 5.264 | — | mradermacher |
| Q5_K_L | 2.79 GiB | 2,992,267,424 | 5.567 | — | bartowski |
| Q6_K | 2.97 GiB | 3,190,740,384 | 5.936 | — | bartowski |
| Q6_K | 2.97 GiB | 3,190,740,544 | 5.936 | — | mradermacher |
| Q6_K_L | 3.12 GiB | 3,353,309,344 | 6.239 | — | bartowski |
| Q8_0 | 3.85 GiB | 4,130,402,464 | 7.684 | — | bartowski |
| Q8_0 | 3.85 GiB | 4,130,402,624 | 7.684 | — | mradermacher |
| BF16 | 7.23 GiB | 7,767,803,776 | 14.451 | — | bartowski |
| F16 | 7.23 GiB | 7,767,804,224 | 14.451 | — | mradermacher |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.25 GiB | 0.53 GiB | 2.14× | 5 / 29 / 0 |
| 8,192 | 0.33 GiB | 1.06 GiB | 3.26× | 5 / 29 / 0 |
| 16,384 | 0.48 GiB | 2.13 GiB | 4.40× | 5 / 29 / 0 |
| 32,768 | 0.79 GiB | 4.25 GiB | 5.35× | 5 / 29 / 0 |
| 65,536 | 1.42 GiB | 8.50 GiB | 5.99× | 5 / 29 / 0 |
| 131,072 | 2.67 GiB | 17.00 GiB | 6.37× | 5 / 29 / 0 |
29 of 34 layers cache only a 1,024-token window rather than the full context, on a period of 6. 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 2.25 GiB. The real file is 2.32 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 4.25 GiB at 32K context where the real figure is 0.79 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-3-4b-it-abliterated need?
- Q4_K_M is exactly 2,489,894,304 bytes (2.32 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-3-4b-it-abliterated's KV cache?
- 0.79 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-3-4b-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.