Can I run diffusiongemma-26B-A4B-it on a Radeon RX 6500 XT?
Not at these settings. No indexed quantization of diffusiongemma-26B-A4B-it fits Radeon RX 6500 XT at any context we compute, with q8_0 KV. The smallest shipped quantization is 9.86 GiB in weights alone, against 3.72 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 | 48.2 | 48.3 | 48.4 | 48.8 | 49.4 | 50.8 |
| Q8_0 | 25.03 GiB | 26.2 | 26.2 | 26.4 | 26.7 | 27.4 | 28.7 |
| Q6_K | 21.10 GiB | 22.2 | 22.3 | 22.5 | 22.8 | 23.5 | 24.8 |
| Q5_K_M | 17.83 GiB | 19.0 | 19.0 | 19.2 | 19.5 | 20.2 | 21.5 |
| Q4_K_M | 15.65 GiB | 16.8 | 16.9 | 17.0 | 17.4 | 18.0 | 19.4 |
| NVFP4 | 13.45 GiB | 14.6 | 14.7 | 14.8 | 15.2 | 15.8 | 17.1 |
| Q3_K_M | 12.38 GiB | 13.5 | 13.6 | 13.8 | 14.1 | 14.8 | 16.1 |
| Q2_K | 9.86 GiB | 11.0 | 11.1 | 11.2 | 11.6 | 12.2 | 13.6 |
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 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.