Does diffusiongemma-26B-A4B-it fit in 8GB of VRAM?
Not at these settings. No indexed quantization of diffusiongemma-26B-A4B-it fits 8GB card at any context we compute, with q4_0 KV. The smallest shipped quantization is 9.86 GiB in weights alone, against 7.44 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| BF16 | 47.07 GiB | 48.0 | 48.0 | 48.1 | 48.3 | 48.6 | 49.3 |
| Q8_0 | 25.03 GiB | 25.9 | 26.0 | 26.1 | 26.3 | 26.6 | 27.3 |
| Q6_K | 21.10 GiB | 22.0 | 22.1 | 22.1 | 22.3 | 22.7 | 23.4 |
| Q5_K_M | 17.83 GiB | 18.7 | 18.8 | 18.9 | 19.1 | 19.4 | 20.1 |
| Q4_K_M | 15.65 GiB | 16.6 | 16.6 | 16.7 | 16.9 | 17.2 | 17.9 |
| NVFP4 | 13.45 GiB | 14.4 | 14.4 | 14.5 | 14.7 | 15.0 | 15.7 |
| Q3_K_M | 12.38 GiB | 13.3 | 13.3 | 13.4 | 13.6 | 14.0 | 14.7 |
| Q2_K | 9.86 GiB | 10.8 | 10.8 | 10.9 | 11.1 | 11.4 | 12.1 |
Figures are GiB of total memory: weights plus KV cache plus compute buffer and backend overhead. Weights and KV are near-exact; the overhead term is modeled. Hover any cell for the breakdown.
Why there are no speeds on this page
A capacity is not a card. Whether a model fits depends only on memory, so every figure above holds for any 8GB accelerator. How fast it runs depends on memory bandwidth, which varies several-fold between cards of the same capacity — so putting a tokens-per-second number here would be inventing one. Pick a specific card from hardware and the speed column appears.
Why other calculators disagree
A parameters × bits ÷ 8 estimate ignores two things that dominate at long context. First, the weights themselves are not the nominal rate — quantizations are mixtures, so the real file is consistently larger than the label implies. Second, most of this model's layers cache only a 1,024-token window rather than the full context.