Does Trinity-Large-Preview fit in 32GB of VRAM?
Not at these settings. No indexed quantization of Trinity-Large-Preview fits 32GB card at any context we compute, with q8_0 KV. The smallest shipped quantization is 76.06 GiB in weights alone, against 29.76 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| BF16 | 742.60 GiB | 743.9 | 744.1 | 744.3 | 744.8 | 745.8 | 747.8 |
| Q8_0 | 394.59 GiB | 395.9 | 396.1 | 396.3 | 396.8 | 397.8 | 399.8 |
| Q6_K_L | 305.35 GiB | 306.7 | 306.8 | 307.1 | 307.6 | 308.6 | 310.6 |
| Q6_K | 305.07 GiB | 306.4 | 306.6 | 306.8 | 307.3 | 308.3 | 310.3 |
| Q5_K_L | 264.22 GiB | 265.5 | 265.7 | 266.0 | 266.5 | 267.5 | 269.4 |
| Q5_K_M | 263.87 GiB | 265.2 | 265.4 | 265.6 | 266.1 | 267.1 | 269.1 |
| Q5_K_S | 255.88 GiB | 257.2 | 257.4 | 257.6 | 258.1 | 259.1 | 261.1 |
| Q4_1 | 232.76 GiB | 234.1 | 234.3 | 234.5 | 235.0 | 236.0 | 238.0 |
| Q4_K_L | 225.46 GiB | 226.8 | 227.0 | 227.2 | 227.7 | 228.7 | 230.7 |
| Q4_K_M | 225.04 GiB | 226.4 | 226.5 | 226.8 | 227.3 | 228.3 | 230.3 |
| Q4_K_S | 217.12 GiB | 218.4 | 218.6 | 218.9 | 219.4 | 220.4 | 222.4 |
| Q4_0 | 213.32 GiB | 214.6 | 214.8 | 215.1 | 215.6 | 216.6 | 218.5 |
| IQ4_NL | 209.71 GiB | 211.0 | 211.2 | 211.5 | 212.0 | 212.9 | 214.9 |
| IQ4_XS | 198.19 GiB | 199.5 | 199.7 | 199.9 | 200.4 | 201.4 | 203.4 |
| Q3_K_M | 176.43 GiB | 177.8 | 177.9 | 178.2 | 178.7 | 179.7 | 181.7 |
| Q3_K_L | 175.82 GiB | 177.1 | 177.3 | 177.6 | 178.1 | 179.1 | 181.0 |
| UD-IQ3_XXS | 170.01 GiB | 171.3 | 171.5 | 171.8 | 172.3 | 173.2 | 175.2 |
| IQ3_M | 168.61 GiB | 169.9 | 170.1 | 170.4 | 170.9 | 171.9 | 173.8 |
| Q3_K_S | 160.91 GiB | 162.2 | 162.4 | 162.7 | 163.1 | 164.1 | 166.1 |
| IQ3_XS | 151.27 GiB | 152.6 | 152.8 | 153.0 | 153.5 | 154.5 | 156.5 |
| IQ3_XXS | 146.20 GiB | 147.5 | 147.7 | 147.9 | 148.4 | 149.4 | 151.4 |
| UD-IQ2_M | 143.68 GiB | 145.0 | 145.2 | 145.4 | 145.9 | 146.9 | 148.9 |
| UD-IQ2_XXS | 142.94 GiB | 144.3 | 144.4 | 144.7 | 145.2 | 146.2 | 148.2 |
| Q2_K_L | 135.17 GiB | 136.5 | 136.7 | 136.9 | 137.4 | 138.4 | 140.4 |
| Q2_K | 135.04 GiB | 136.4 | 136.5 | 136.8 | 137.3 | 138.3 | 140.3 |
| IQ2_M | 116.50 GiB | 117.8 | 118.0 | 118.2 | 118.7 | 119.7 | 121.7 |
| IQ2_S | 102.49 GiB | 103.8 | 104.0 | 104.2 | 104.7 | 105.7 | 107.7 |
| IQ2_XS | 102.27 GiB | 103.6 | 103.8 | 104.0 | 104.5 | 105.5 | 107.5 |
| IQ2_XXS | 88.58 GiB | 89.9 | 90.1 | 90.3 | 90.8 | 91.8 | 93.8 |
| IQ1_M | 79.29 GiB | 80.6 | 80.8 | 81.0 | 81.5 | 82.5 | 84.5 |
| IQ1_S | 76.06 GiB | 77.4 | 77.6 | 77.8 | 78.3 | 79.3 | 81.3 |
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 32GB 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 4,096-token window rather than the full context.