Does MiMo-V2.5 fit in 24GB of VRAM?
Not at these settings. No indexed quantization of MiMo-V2.5 fits 24GB card at any context we compute, with q4_0 KV. The smallest shipped quantization is 60.09 GiB in weights alone, against 22.32 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| BF16 | 577.08 GiB | 578.1 | 578.2 | 578.5 | 579.0 | 580.0 | 582.2 |
| Q8_0 | 306.67 GiB | 307.6 | 307.8 | 308.0 | 308.6 | 309.6 | 311.7 |
| Q6_K | 249.18 GiB | 250.2 | 250.3 | 250.6 | 251.1 | 252.1 | 254.2 |
| UD-Q6_K | 239.34 GiB | 240.3 | 240.4 | 240.7 | 241.2 | 242.3 | 244.4 |
| UD-Q5_K_M | 214.37 GiB | 215.3 | 215.5 | 215.7 | 216.3 | 217.3 | 219.4 |
| Q5_K_M | 206.24 GiB | 207.2 | 207.4 | 207.6 | 208.1 | 209.2 | 211.3 |
| UD-Q5_K_S | 201.45 GiB | 202.4 | 202.6 | 202.8 | 203.3 | 204.4 | 206.5 |
| Q5_K_S | 199.54 GiB | 200.5 | 200.7 | 200.9 | 201.4 | 202.5 | 204.6 |
| Q4_1 | 180.99 GiB | 182.0 | 182.1 | 182.4 | 182.9 | 183.9 | 186.1 |
| UD-Q4_K_M | 177.81 GiB | 178.8 | 178.9 | 179.2 | 179.7 | 180.8 | 182.9 |
| Q4_K_L | 176.24 GiB | 177.2 | 177.3 | 177.6 | 178.1 | 179.2 | 181.3 |
| Q4_K_M | 175.80 GiB | 176.8 | 176.9 | 177.2 | 177.7 | 178.8 | 180.9 |
| Q4_K_S | 169.41 GiB | 170.4 | 170.5 | 170.8 | 171.3 | 172.4 | 174.5 |
| UD-Q4_K_S | 166.56 GiB | 167.5 | 167.7 | 167.9 | 168.5 | 169.5 | 171.6 |
| Q4_K | 165.75 GiB | 166.7 | 166.9 | 167.1 | 167.7 | 168.7 | 170.8 |
| Q4_0 | 163.68 GiB | 164.7 | 164.8 | 165.1 | 165.6 | 166.6 | 168.7 |
| IQ4_NL | 163.24 GiB | 164.2 | 164.4 | 164.6 | 165.1 | 166.2 | 168.3 |
| IQ4_XS | 154.32 GiB | 155.3 | 155.4 | 155.7 | 156.2 | 157.3 | 159.4 |
| UD-IQ4_NL | 142.12 GiB | 143.1 | 143.2 | 143.5 | 144.0 | 145.1 | 147.2 |
| UD-IQ4_XS | 139.18 GiB | 140.2 | 140.3 | 140.6 | 141.1 | 142.1 | 144.2 |
| IQ3_M | 138.15 GiB | 139.1 | 139.3 | 139.5 | 140.1 | 141.1 | 143.2 |
| Q3_K_L | 137.77 GiB | 138.8 | 138.9 | 139.1 | 139.7 | 140.7 | 142.8 |
| Q3_K_M | 132.47 GiB | 133.5 | 133.6 | 133.8 | 134.4 | 135.4 | 137.5 |
| IQ3_XS | 132.46 GiB | 133.4 | 133.6 | 133.8 | 134.4 | 135.4 | 137.5 |
| UD-Q3_K_M | 130.41 GiB | 131.4 | 131.5 | 131.8 | 132.3 | 133.4 | 135.5 |
| Q3_K_S | 125.83 GiB | 126.8 | 126.9 | 127.2 | 127.7 | 128.8 | 130.9 |
| IQ3_XXS | 121.31 GiB | 122.3 | 122.4 | 122.7 | 123.2 | 124.3 | 126.4 |
| UD-IQ3_XXS | 117.27 GiB | 118.3 | 118.4 | 118.6 | 119.2 | 120.2 | 122.3 |
| UD-IQ3_S | 106.98 GiB | 108.0 | 108.1 | 108.4 | 108.9 | 109.9 | 112.0 |
| IQ3_S | 106.20 GiB | 107.2 | 107.3 | 107.6 | 108.1 | 109.2 | 111.3 |
| Q2_K_L | 102.02 GiB | 103.0 | 103.1 | 103.4 | 103.9 | 105.0 | 107.1 |
| Q2_K | 101.46 GiB | 102.4 | 102.6 | 102.8 | 103.4 | 104.4 | 106.5 |
| IQ2_M | 97.30 GiB | 98.3 | 98.4 | 98.7 | 99.2 | 100.3 | 102.4 |
| UD-IQ2_M | 89.93 GiB | 90.9 | 91.0 | 91.3 | 91.8 | 92.9 | 95.0 |
| UD-IQ2_XXS | 89.85 GiB | 90.8 | 91.0 | 91.2 | 91.8 | 92.8 | 94.9 |
| IQ2_S | 88.18 GiB | 89.2 | 89.3 | 89.6 | 90.1 | 91.1 | 93.2 |
| IQ2_XS | 86.68 GiB | 87.7 | 87.8 | 88.1 | 88.6 | 89.6 | 91.7 |
| UD-IQ1_M | 86.17 GiB | 87.1 | 87.3 | 87.5 | 88.1 | 89.1 | 91.2 |
| IQ2_XXS | 79.83 GiB | 80.8 | 80.9 | 81.2 | 81.7 | 82.8 | 84.9 |
| IQ1_M | 67.01 GiB | 68.0 | 68.1 | 68.4 | 68.9 | 70.0 | 72.1 |
| IQ1_S | 60.09 GiB | 61.1 | 61.2 | 61.5 | 62.0 | 63.0 | 65.2 |
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 there are no speeds on this page
A capacity is not a card. Whether a model fits depends only on memory, so every figure above holds for any 24GB accelerator. How fast it runs depends on memory bandwidth, which varies several-fold between cards of the same capacity — so putting a tokens-per-second number here would be inventing one. Pick a specific card from hardware and the speed column appears.
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 128-token window rather than the full context.