Can I run Kimi-Dev-72B on a Apple M3 Pro?
Not at these settings. No indexed quantization of Kimi-Dev-72B fits Apple M3 Pro at any context we compute, with q8_0 KV. The smallest shipped quantization is 21.47 GiB in weights alone, against 12.56 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| BF16 | 135.44 GiB | 136.8 | 137.4 | 138.8 | 141.4 | 146.7 | 157.4 |
| Q8_0 | 71.96 GiB | 73.3 | 74.0 | 75.3 | 77.9 | 83.3 | 93.9 |
| Q6_K | 59.93 GiB | 61.3 | 61.9 | 63.3 | 65.9 | 71.2 | 81.9 |
| Q4_1 | 42.56 GiB | 43.9 | 44.6 | 45.9 | 48.6 | 53.9 | 64.5 |
| Q4_0 | 38.54 GiB | 39.9 | 40.5 | 41.9 | 44.5 | 49.8 | 60.5 |
| IQ4_NL | 38.48 GiB | 39.8 | 40.5 | 41.8 | 44.5 | 49.8 | 60.4 |
| IQ4_XS | 37.02 GiB | 38.4 | 39.0 | 40.4 | 43.0 | 48.3 | 58.9 |
| Q3_K_M | 35.11 GiB | 36.5 | 37.1 | 38.4 | 41.1 | 46.4 | 57.0 |
| Q3_K_S | 32.12 GiB | 33.5 | 34.1 | 35.5 | 38.1 | 43.4 | 54.0 |
| UD-IQ3_XXS | 29.67 GiB | 31.0 | 31.7 | 33.0 | 35.7 | 41.0 | 51.6 |
| UD-IQ2_M | 27.56 GiB | 28.9 | 29.6 | 30.9 | 33.6 | 38.9 | 49.5 |
| UD-IQ2_XXS | 23.94 GiB | 25.3 | 25.9 | 27.3 | 29.9 | 35.2 | 45.9 |
| UD-IQ1_M | 22.35 GiB | 23.7 | 24.4 | 25.7 | 28.3 | 33.7 | 44.3 |
| UD-IQ1_S | 21.47 GiB | 22.8 | 23.5 | 24.8 | 27.5 | 32.8 | 43.4 |
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.