Does cogito-671b-v2.1 fit in 48GB of VRAM?
Not at these settings. No indexed quantization of cogito-671b-v2.1 fits 48GB card at any context we compute, with q8_0 KV. The smallest shipped quantization is 138.82 GiB in weights alone, against 44.64 GiB usable. CPU offload can still run it, slowly.
Every quantization at every context
| Quant | Weights● | 4K◐ | 8K◐ | 16K◐ | 32K◐ | 64K◐ | 128K◐ |
|---|---|---|---|---|---|---|---|
| BF16 | 1250.08 GiB | 1251.1 | 1251.2 | 1251.5 | 1252.1 | 1253.2 | 1255.5 |
| Q8_0 | 664.29 GiB | 665.3 | 665.5 | 665.7 | 666.3 | 667.5 | 669.7 |
| Q6_K | 513.45 GiB | 514.5 | 514.6 | 514.9 | 515.5 | 516.6 | 518.9 |
| Q5_K_M | 443.46 GiB | 444.5 | 444.6 | 444.9 | 445.5 | 446.6 | 448.9 |
| Q5_K_S | 430.86 GiB | 431.9 | 432.0 | 432.3 | 432.9 | 434.0 | 436.3 |
| Q4_1 | 391.87 GiB | 392.9 | 393.0 | 393.3 | 393.9 | 395.0 | 397.3 |
| Q4_K_M | 377.55 GiB | 378.6 | 378.7 | 379.0 | 379.6 | 380.7 | 383.0 |
| Q4_K_S | 354.89 GiB | 355.9 | 356.1 | 356.3 | 356.9 | 358.0 | 360.3 |
| Q4_0 | 353.99 GiB | 355.0 | 355.2 | 355.4 | 356.0 | 357.1 | 359.4 |
| IQ4_NL | 353.09 GiB | 354.1 | 354.3 | 354.5 | 355.1 | 356.3 | 358.5 |
| IQ4_XS | 333.73 GiB | 334.8 | 334.9 | 335.2 | 335.8 | 336.9 | 339.2 |
| Q3_K_M | 298.44 GiB | 299.5 | 299.6 | 299.9 | 300.5 | 301.6 | 303.9 |
| Q3_K_S | 270.50 GiB | 271.5 | 271.7 | 271.9 | 272.5 | 273.7 | 275.9 |
| UD-IQ3_XXS | 254.72 GiB | 255.7 | 255.9 | 256.2 | 256.7 | 257.9 | 260.2 |
| Q2_K_L | 229.02 GiB | 230.0 | 230.2 | 230.5 | 231.0 | 232.2 | 234.5 |
| Q2_K | 228.82 GiB | 229.8 | 230.0 | 230.3 | 230.8 | 232.0 | 234.3 |
| UD-IQ2_M | 212.81 GiB | 213.8 | 214.0 | 214.3 | 214.8 | 216.0 | 218.3 |
| UD-IQ2_XXS | 201.88 GiB | 202.9 | 203.0 | 203.3 | 203.9 | 205.0 | 207.3 |
| UD-IQ1_M | 187.19 GiB | 188.2 | 188.4 | 188.6 | 189.2 | 190.4 | 192.6 |
| UD-IQ1_S | 173.11 GiB | 174.1 | 174.3 | 174.6 | 175.1 | 176.3 | 178.5 |
| IQ2_XXS | 162.59 GiB | 163.6 | 163.8 | 164.0 | 164.6 | 165.7 | 168.0 |
| UD-TQ1_0 | 150.80 GiB | 151.8 | 152.0 | 152.2 | 152.8 | 154.0 | 156.2 |
| IQ1_M | 138.82 GiB | 139.8 | 140.0 | 140.3 | 140.8 | 142.0 | 144.3 |
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 48GB 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, this model uses latent attention and allocates no V cache at all, so any formula reading num_key_value_heads overstates its cache by more than an order of magnitude.