Model comparison
gpt2 vs tinygemma3_cifar
These two publish different quantization sets; the table below has the exact sizes.
From the file· summed bytes, KV per layer
Side by side
| gpt2 | tinygemma3_cifar | |
|---|---|---|
| Parameters | 137M | 39M |
| Architecture | gpt2 | gemma3 |
| Layers | 12 | 8 |
| Native context | — | 131,072 |
| Mixture of experts | no | no |
| Quantizations published | 49 | 1 |
| Smallest quantization | 0.06 GiB | 0.04 GiB |
| Q4_K_M | 0.11 GiB | — |
| Licence | — | wtfpl |
KV cache by context
the term that decides long-context viability
| Context | gpt2 | tinygemma3_cifar | Ratio |
|---|---|---|---|
| 4,096 | — | 0.06 GiB | — |
| 8,192 | — | 0.08 GiB | — |
| 16,384 | — | 0.09 GiB | — |
| 32,768 | — | 0.12 GiB | — |
| 65,536 | — | 0.19 GiB | — |
| 131,072 | — | 0.31 GiB | — |