Can I run medgemma-27b-text-it on a Radeon RX 6500 XT?
Not at these settings. No indexed quantization of medgemma-27b-text-it fits Radeon RX 6500 XT at any context we compute, with q4_0 KV. The smallest shipped quantization is 6.06 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 | 50.32 GiB | 51.6 | 51.6 | 51.8 | 52.2 | 52.9 | 54.3 |
| Q8_0 | 26.74 GiB | 28.0 | 28.1 | 28.2 | 28.6 | 29.3 | 30.7 |
| Q6_K | 20.64 GiB | 21.9 | 22.0 | 22.1 | 22.5 | 23.2 | 24.6 |
| Q5_K_M | 17.95 GiB | 19.2 | 19.3 | 19.5 | 19.8 | 20.5 | 21.9 |
| Q5_K_S | 17.48 GiB | 18.7 | 18.8 | 19.0 | 19.3 | 20.0 | 21.4 |
| Q4_1 | 15.99 GiB | 17.2 | 17.3 | 17.5 | 17.8 | 18.5 | 20.0 |
| Q4_K_M | 15.41 GiB | 16.7 | 16.7 | 16.9 | 17.3 | 18.0 | 19.4 |
| Q4_K_S | 14.60 GiB | 15.8 | 15.9 | 16.1 | 16.5 | 17.2 | 18.6 |
| IQ4_NL | 14.50 GiB | 15.7 | 15.8 | 16.0 | 16.4 | 17.1 | 18.5 |
| IQ4_XS | 13.75 GiB | 15.0 | 15.1 | 15.3 | 15.6 | 16.3 | 17.7 |
| Q3_K_L | 13.54 GiB | 14.8 | 14.9 | 15.0 | 15.4 | 16.1 | 17.5 |
| Q3_K_M | 12.51 GiB | 13.8 | 13.8 | 14.0 | 14.4 | 15.1 | 16.5 |
| Q3_K_S | 11.33 GiB | 12.6 | 12.7 | 12.8 | 13.2 | 13.9 | 15.3 |
| UD-IQ3_XXS | 10.07 GiB | 11.3 | 11.4 | 11.6 | 11.9 | 12.6 | 14.0 |
| Q2_K | 9.78 GiB | 11.0 | 11.1 | 11.3 | 11.6 | 12.3 | 13.7 |
| Q2_K_L | 9.78 GiB | 11.0 | 11.1 | 11.3 | 11.6 | 12.3 | 13.7 |
| UD-IQ2_M | 8.96 GiB | 10.2 | 10.3 | 10.5 | 10.8 | 11.5 | 12.9 |
| UD-IQ2_XXS | 7.31 GiB | 8.6 | 8.6 | 8.8 | 9.2 | 9.9 | 11.3 |
| UD-IQ1_M | 6.51 GiB | 7.7 | 7.8 | 8.0 | 8.4 | 9.1 | 10.5 |
| UD-IQ1_S | 6.06 GiB | 7.3 | 7.4 | 7.6 | 7.9 | 8.6 | 10.0 |
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.