Nidum-Gemma-3-27B-it-Uncensored
osmapi/Nidum-Gemma-3-27B-it-UncensoredNidum-Gemma-3-27B-it-Uncensored at Q4_K_M is exactly 16,546,688,896 bytes (15.41 GiB / 16.55 GB) — an effective 4.825 bits per weight, not the nominal 4. Its KV cache at 32K is 3.11 GiB, not the 15.50 GiB a flat formula predicts.
Shipped quantizations
| Quant | Size● | Exact bytes● | Effective bpw● | Tensors● | Publisher |
|---|---|---|---|---|---|
| I1-IQ1_S | 5.83 GiB | 6,264,247,808 | 1.827 | — | mradermacher |
| I1-IQ1_M | 6.33 GiB | 6,797,245,952 | 1.982 | — | mradermacher |
| I1-IQ2_XXS | 7.16 GiB | 7,685,576,192 | 2.241 | — | mradermacher |
| I1-IQ2_XS | 7.86 GiB | 8,438,904,320 | 2.461 | — | mradermacher |
| I1-IQ2_S | 8.18 GiB | 8,782,409,216 | 2.561 | — | mradermacher |
| I1-IQ2_M | 8.84 GiB | 9,493,073,408 | 2.768 | — | mradermacher |
| I1-Q2_K_S | 9.09 GiB | 9,757,446,272 | 2.845 | — | mradermacher |
| Q2_K | 9.78 GiB | 10,503,720,832 | 3.063 | — | mradermacher |
| I1-Q2_K | 9.78 GiB | 10,503,721,088 | 3.063 | — | mradermacher |
| I1-IQ3_XXS | 9.98 GiB | 10,716,478,976 | 3.125 | — | mradermacher |
| I1-IQ3_XS | 10.77 GiB | 11,562,233,984 | 3.372 | — | mradermacher |
| Q3_K_S | 11.33 GiB | 12,167,614,336 | 3.548 | — | mradermacher |
| I1-Q3_K_S | 11.33 GiB | 12,167,614,592 | 3.548 | — | mradermacher |
| I1-IQ3_S | 11.33 GiB | 12,167,614,592 | 3.548 | — | mradermacher |
| I1-IQ3_M | 11.69 GiB | 12,547,074,176 | 3.659 | — | mradermacher |
| Q3_K_M | 12.51 GiB | 13,437,640,576 | 3.919 | — | mradermacher |
| I1-Q3_K_M | 12.51 GiB | 13,437,640,832 | 3.919 | — | mradermacher |
| Q3_K_L | 13.54 GiB | 14,543,462,272 | 4.241 | — | mradermacher |
| I1-Q3_K_L | 13.54 GiB | 14,543,462,528 | 4.241 | — | mradermacher |
| I1-IQ4_XS | 13.75 GiB | 14,767,448,192 | 4.307 | — | mradermacher |
| IQ4_XS | 13.87 GiB | 14,893,891,456 | 4.343 | — | mradermacher |
| I1-Q4_0 | 14.55 GiB | 15,617,974,400 | 4.555 | — | mradermacher |
| Q4_K_S | 14.60 GiB | 15,674,056,576 | 4.571 | — | mradermacher |
| I1-Q4_K_S | 14.60 GiB | 15,674,056,832 | 4.571 | — | mradermacher |
| Q4_K_M | 15.41 GiB | 16,546,688,896 | 4.825 | — | mradermacher |
| I1-Q4_K_M | 15.41 GiB | 16,546,689,152 | 4.825 | — | mradermacher |
| I1-Q4_1 | 15.99 GiB | 17,167,294,592 | 5.006 | — | mradermacher |
| Q5_K_S | 17.48 GiB | 18,767,191,936 | 5.473 | — | mradermacher |
| I1-Q5_K_S | 17.48 GiB | 18,767,192,192 | 5.473 | — | mradermacher |
| Q5_K_M | 17.95 GiB | 19,271,675,776 | 5.620 | — | mradermacher |
| I1-Q5_K_M | 17.95 GiB | 19,271,676,032 | 5.620 | — | mradermacher |
| Q6_K | 20.64 GiB | 22,166,974,336 | 6.465 | — | mradermacher |
| I1-Q6_K | 20.64 GiB | 22,166,974,592 | 6.465 | — | mradermacher |
| Q8_0 | 26.74 GiB | 28,707,972,352 | 8.372 | — | mradermacher |
KV cache by context
| Context | KV cache (f16)● | Flat formula | Overstated by | Full / windowed / recurrent |
|---|---|---|---|---|
| 4,096 | 0.92 GiB | 1.94 GiB | 2.10× | 10 / 52 / 0 |
| 8,192 | 1.23 GiB | 3.88 GiB | 3.14× | 10 / 52 / 0 |
| 16,384 | 1.86 GiB | 7.75 GiB | 4.17× | 10 / 52 / 0 |
| 32,768 | 3.11 GiB | 15.50 GiB | 4.98× | 10 / 52 / 0 |
| 65,536 | 5.61 GiB | 31.00 GiB | 5.53× | 10 / 52 / 0 |
| 131,072 | 10.61 GiB | 62.00 GiB | 5.84× | 10 / 52 / 0 |
52 of 62 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 14.37 GiB. The real file is 15.41 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 15.50 GiB at 32K context where the real figure is 3.11 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 Nidum-Gemma-3-27B-it-Uncensored need?
- Q4_K_M is exactly 16,546,688,896 bytes (15.41 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
- How large is Nidum-Gemma-3-27B-it-Uncensored's KV cache?
- 3.11 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 Nidum-Gemma-3-27B-it-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.