Can I run functionary-medium-v3.2 on a Radeon RX 6500 XT?
Not at these settings. No indexed quantization of functionary-medium-v3.2 fits Radeon RX 6500 XT at any context we compute, with q8_0 KV. The smallest shipped quantization is 15.60 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◐ |
|---|---|---|---|---|---|---|---|
| Q8_0 | 69.83 GiB | 71.5 | 72.2 | 73.5 | 76.2 | 81.5 | 92.1 |
| Q6_K | 53.91 GiB | 55.6 | 56.3 | 57.6 | 60.3 | 65.6 | 76.2 |
| Q5_K_M | 46.52 GiB | 48.2 | 48.9 | 50.2 | 52.9 | 58.2 | 68.8 |
| Q4_K_L | 40.33 GiB | 42.0 | 42.7 | 44.0 | 46.7 | 52.0 | 62.6 |
| Q4_K_M | 39.60 GiB | 41.3 | 42.0 | 43.3 | 45.9 | 51.3 | 61.9 |
| Q4_K_S | 37.58 GiB | 39.3 | 39.9 | 41.3 | 43.9 | 49.2 | 59.9 |
| IQ4_XS | 35.30 GiB | 37.0 | 37.7 | 39.0 | 41.6 | 47.0 | 57.6 |
| Q3_K_L | 34.59 GiB | 36.3 | 36.9 | 38.3 | 40.9 | 46.2 | 56.9 |
| Q3_K_M | 31.91 GiB | 33.6 | 34.3 | 35.6 | 38.3 | 43.6 | 54.2 |
| IQ3_M | 29.74 GiB | 31.4 | 32.1 | 33.4 | 36.1 | 41.4 | 52.0 |
| Q3_K_S | 28.79 GiB | 30.5 | 31.1 | 32.5 | 35.1 | 40.4 | 51.1 |
| IQ3_XXS | 25.58 GiB | 27.3 | 27.9 | 29.3 | 31.9 | 37.2 | 47.9 |
| Q2_K_L | 25.52 GiB | 27.2 | 27.9 | 29.2 | 31.9 | 37.2 | 47.8 |
| Q2_K | 24.56 GiB | 26.3 | 26.9 | 28.2 | 30.9 | 36.2 | 46.8 |
| IQ2_M | 22.46 GiB | 24.2 | 24.8 | 26.1 | 28.8 | 34.1 | 44.7 |
| IQ2_XS | 19.69 GiB | 21.4 | 22.0 | 23.4 | 26.0 | 31.3 | 42.0 |
| IQ2_XXS | 17.79 GiB | 19.5 | 20.1 | 21.5 | 24.1 | 29.4 | 40.1 |
| IQ1_M | 15.60 GiB | 17.3 | 18.0 | 19.3 | 21.9 | 27.3 | 37.9 |
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 8,192-token window rather than the full context.