Can I run Qwen2.5-32B-Instruct on a RTX A2000?
Not at these settings. No indexed quantization of Qwen2.5-32B-Instruct fits RTX A2000 at any context we compute, with q4_0 KV. The smallest shipped quantization is 8.41 GiB in weights alone, against 5.58 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| F16 | 61.04 GiB | 62.4 | 62.7 | 63.3 | 64.4 | 66.6 | 71.1 |
| Q8_0 | 32.43 GiB | 33.8 | 34.1 | 34.7 | 35.8 | 38.0 | 42.5 |
| Q6_K_L | 25.39 GiB | 26.8 | 27.1 | 27.6 | 28.7 | 31.0 | 35.5 |
| Q6_K | 25.04 GiB | 26.4 | 26.7 | 27.3 | 28.4 | 30.6 | 35.1 |
| Q5_K_L | 22.11 GiB | 23.5 | 23.8 | 24.3 | 25.5 | 27.7 | 32.2 |
| Q5_K_M | 21.66 GiB | 23.0 | 23.3 | 23.9 | 25.0 | 27.3 | 31.8 |
| Q5_K_S | 21.08 GiB | 22.5 | 22.7 | 23.3 | 24.4 | 26.7 | 31.2 |
| Q5_0 | 21.08 GiB | 22.5 | 22.7 | 23.3 | 24.4 | 26.7 | 31.2 |
| Q4_K_L | 19.03 GiB | 20.4 | 20.7 | 21.3 | 22.4 | 24.6 | 29.1 |
| Q4_K_M | 18.49 GiB | 19.9 | 20.1 | 20.7 | 21.8 | 24.1 | 28.6 |
| Q4_K_S | 17.49 GiB | 18.9 | 19.2 | 19.7 | 20.8 | 23.1 | 27.6 |
| Q4_0 | 17.43 GiB | 18.8 | 19.1 | 19.7 | 20.8 | 23.0 | 27.5 |
| IQ4_XS | 16.48 GiB | 17.9 | 18.1 | 18.7 | 19.8 | 22.1 | 26.6 |
| Q3_K_L | 16.06 GiB | 17.4 | 17.7 | 18.3 | 19.4 | 21.7 | 26.2 |
| Q3_K_M | 14.84 GiB | 16.2 | 16.5 | 17.1 | 18.2 | 20.4 | 24.9 |
| IQ3_M | 13.79 GiB | 15.2 | 15.5 | 16.0 | 17.1 | 19.4 | 23.9 |
| Q3_K_S | 13.40 GiB | 14.8 | 15.1 | 15.6 | 16.8 | 19.0 | 23.5 |
| IQ3_XS | 12.76 GiB | 14.1 | 14.4 | 15.0 | 16.1 | 18.4 | 22.9 |
| Q2_K_L | 12.18 GiB | 13.6 | 13.8 | 14.4 | 15.5 | 17.8 | 22.3 |
| Q2_K | 11.47 GiB | 12.8 | 13.1 | 13.7 | 14.8 | 17.1 | 21.6 |
| IQ2_M | 10.49 GiB | 11.9 | 12.2 | 12.7 | 13.8 | 16.1 | 20.6 |
| IQ2_S | 9.67 GiB | 11.1 | 11.3 | 11.9 | 13.0 | 15.3 | 19.8 |
| IQ2_XS | 9.27 GiB | 10.7 | 10.9 | 11.5 | 12.6 | 14.9 | 19.4 |
| IQ2_XXS | 8.41 GiB | 9.8 | 10.1 | 10.6 | 11.8 | 14.0 | 18.5 |
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 131,072-token window rather than the full context.