Model comparison
metatune-gpt20b-R1.09 vs gemma-4-12B-it-qat-q4_0-unquantized
These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: metatune-gpt20b-R1.09's KV cache at 32K is 3.2× smaller, which usually matters more than the difference in weights.
From the file· summed bytes, KV per layer
Side by side
| metatune-gpt20b-R1.09 | gemma-4-12B-it-qat-q4_0-unquantized | |
|---|---|---|
| Parameters | 21.5B | 12.0B |
| Architecture | gpt-oss | gemma4 |
| Layers | 24 | 48 |
| Native context | 131,072 | 262,144 |
| Mixture of experts | yes, 32 experts | no |
| Quantizations published | 34 | 2 |
| Smallest quantization | 11.19 GiB | 6.50 GiB |
| Q4_K_M | 14.72 GiB | — |
| Licence | apache-2.0 | apache-2.0 |
KV cache by context
the term that decides long-context viability
| Context | metatune-gpt20b-R1.09 | gemma-4-12B-it-qat-q4_0-unquantized | Ratio |
|---|---|---|---|
| 4,096 | 0.11 GiB | 0.72 GiB | 6.46× |
| 8,192 | 0.21 GiB | 0.97 GiB | 4.72× |
| 16,384 | 0.39 GiB | 1.47 GiB | 3.74× |
| 32,768 | 0.77 GiB | 2.47 GiB | 3.22× |
| 65,536 | 1.52 GiB | 4.47 GiB | 2.94× |
| 131,072 | 3.02 GiB | 8.47 GiB | 2.81× |