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 q4_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 | 71.2 | 71.6 | 72.3 | 73.7 | 76.5 | 82.1 |
| Q6_K | 53.91 GiB | 55.3 | 55.6 | 56.3 | 57.8 | 60.6 | 66.2 |
| Q5_K_M | 46.52 GiB | 47.9 | 48.2 | 49.0 | 50.4 | 53.2 | 58.8 |
| Q4_K_L | 40.33 GiB | 41.7 | 42.1 | 42.8 | 44.2 | 47.0 | 52.6 |
| Q4_K_M | 39.60 GiB | 41.0 | 41.3 | 42.0 | 43.4 | 46.3 | 51.9 |
| Q4_K_S | 37.58 GiB | 39.0 | 39.3 | 40.0 | 41.4 | 44.2 | 49.9 |
| IQ4_XS | 35.30 GiB | 36.7 | 37.0 | 37.7 | 39.1 | 42.0 | 47.6 |
| Q3_K_L | 34.59 GiB | 36.0 | 36.3 | 37.0 | 38.4 | 41.2 | 46.9 |
| Q3_K_M | 31.91 GiB | 33.3 | 33.6 | 34.3 | 35.8 | 38.6 | 44.2 |
| IQ3_M | 29.74 GiB | 31.1 | 31.5 | 32.2 | 33.6 | 36.4 | 42.0 |
| Q3_K_S | 28.79 GiB | 30.2 | 30.5 | 31.2 | 32.6 | 35.4 | 41.1 |
| IQ3_XXS | 25.58 GiB | 27.0 | 27.3 | 28.0 | 29.4 | 32.2 | 37.9 |
| Q2_K_L | 25.52 GiB | 26.9 | 27.2 | 28.0 | 29.4 | 32.2 | 37.8 |
| Q2_K | 24.56 GiB | 25.9 | 26.3 | 27.0 | 28.4 | 31.2 | 36.8 |
| IQ2_M | 22.46 GiB | 23.8 | 24.2 | 24.9 | 26.3 | 29.1 | 34.7 |
| IQ2_XS | 19.69 GiB | 21.1 | 21.4 | 22.1 | 23.5 | 26.3 | 32.0 |
| IQ2_XXS | 17.79 GiB | 19.2 | 19.5 | 20.2 | 21.6 | 24.4 | 30.1 |
| IQ1_M | 15.60 GiB | 17.0 | 17.3 | 18.0 | 19.4 | 22.3 | 27.9 |
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.