Model comparison
gemma-4-12B-it-qat-q4_0-unquantized vs NVIDIA-Nemotron-Nano-12B-v2
These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: gemma-4-12B-it-qat-q4_0-unquantized's KV cache at 32K is 3.1× smaller, which usually matters more than the difference in weights.
From the file· summed bytes, KV per layer
Side by side
| gemma-4-12B-it-qat-q4_0-unquantized | NVIDIA-Nemotron-Nano-12B-v2 | |
|---|---|---|
| Parameters | 12.0B | 12.3B |
| Architecture | gemma4 | nemotron_h |
| Layers | 48 | 62 |
| Native context | 262,144 | 131,072 |
| Mixture of experts | no | no |
| Quantizations published | 2 | 30 |
| Smallest quantization | 6.50 GiB | 3.79 GiB |
| Q4_K_M | — | 6.98 GiB |
| Licence | apache-2.0 | — |
KV cache by context
the term that decides long-context viability
| Context | gemma-4-12B-it-qat-q4_0-unquantized | NVIDIA-Nemotron-Nano-12B-v2 | Ratio |
|---|---|---|---|
| 4,096 | 0.72 GiB | 0.97 GiB | 1.35× |
| 8,192 | 0.97 GiB | 1.94 GiB | 2.00× |
| 16,384 | 1.47 GiB | 3.88 GiB | 2.64× |
| 32,768 | 2.47 GiB | 7.75 GiB | 3.14× |
| 65,536 | 4.47 GiB | 15.50 GiB | 3.47× |
| 131,072 | 8.47 GiB | 31.00 GiB | 3.66× |