Can I run Ministral-8B-Instruct-2410 on a Radeon RX 6500 XT?
Not at these settings. No indexed quantization of Ministral-8B-Instruct-2410 fits Radeon RX 6500 XT at any context we compute, with q4_0 KV. The smallest shipped quantization is 2.76 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◐ |
|---|---|---|---|---|---|---|---|
| F16 | 14.95 GiB | 16.0 | 16.2 | 16.5 | 17.1 | 17.5 | 18.1 |
| Q8_0 | 7.94 GiB | 9.0 | 9.2 | 9.5 | 10.1 | 10.5 | 11.1 |
| Q6_K_L | 6.38 GiB | 7.5 | 7.6 | 7.9 | 8.6 | 8.9 | 9.5 |
| Q6_K | 6.14 GiB | 7.2 | 7.4 | 7.7 | 8.3 | 8.7 | 9.3 |
| Q5_K_L | 5.64 GiB | 6.7 | 6.9 | 7.2 | 7.8 | 8.2 | 8.8 |
| Q5_K_M | 5.33 GiB | 6.4 | 6.6 | 6.9 | 7.5 | 7.9 | 8.5 |
| Q5_K_S | 5.21 GiB | 6.3 | 6.5 | 6.8 | 7.4 | 7.7 | 8.4 |
| Q4_K_L | 4.95 GiB | 6.0 | 6.2 | 6.5 | 7.1 | 7.5 | 8.1 |
| Q4_K_M | 4.57 GiB | 5.7 | 5.8 | 6.1 | 6.8 | 7.1 | 7.7 |
| Q4_K_S | 4.36 GiB | 5.5 | 5.6 | 5.9 | 6.6 | 6.9 | 7.5 |
| Q4_0 | 4.35 GiB | 5.4 | 5.6 | 5.9 | 6.5 | 6.9 | 7.5 |
| IQ4_XS | 4.14 GiB | 5.2 | 5.4 | 5.7 | 6.3 | 6.7 | 7.3 |
| Q3_K_L | 4.03 GiB | 5.1 | 5.3 | 5.6 | 6.2 | 6.6 | 7.2 |
| Q3_K_M | 3.74 GiB | 4.8 | 5.0 | 5.3 | 5.9 | 6.3 | 6.9 |
| IQ3_M | 3.53 GiB | 4.6 | 4.8 | 5.1 | 5.7 | 6.1 | 6.7 |
| Q2_K_L | 3.45 GiB | 4.5 | 4.7 | 5.0 | 5.7 | 6.0 | 6.6 |
| Q3_K_S | 3.41 GiB | 4.5 | 4.7 | 5.0 | 5.6 | 5.9 | 6.6 |
| IQ3_XS | 3.28 GiB | 4.4 | 4.5 | 4.8 | 5.5 | 5.8 | 6.4 |
| Q2_K | 2.97 GiB | 4.1 | 4.2 | 4.5 | 5.2 | 5.5 | 6.1 |
| IQ2_M | 2.76 GiB | 3.8 | 4.0 | 4.3 | 5.0 | 5.3 | 5.9 |
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 32,768-token window rather than the full context.