Does DeepSeek-TNG-R1T2-Chimera fit in 32GB of VRAM?
Not at these settings. No indexed quantization of DeepSeek-TNG-R1T2-Chimera fits 32GB card at any context we compute, with q8_0 KV. The smallest shipped quantization is 150.08 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 | 1250.09 GiB | 1251.1 | 1251.3 | 1251.5 | 1252.1 | 1253.2 | 1255.5 |
| Q8_0 | 664.30 GiB | 665.3 | 665.5 | 665.7 | 666.3 | 667.5 | 669.7 |
| Q6_K | 513.20 GiB | 514.2 | 514.4 | 514.7 | 515.2 | 516.4 | 518.6 |
| Q5_K_M | 443.10 GiB | 444.1 | 444.3 | 444.6 | 445.1 | 446.3 | 448.5 |
| Q5_K_S | 430.46 GiB | 431.5 | 431.6 | 431.9 | 432.5 | 433.6 | 435.9 |
| Q4_1 | 391.52 GiB | 392.5 | 392.7 | 393.0 | 393.5 | 394.7 | 397.0 |
| Q4_K_M | 377.13 GiB | 378.2 | 378.3 | 378.6 | 379.1 | 380.3 | 382.6 |
| Q4_K_S | 354.38 GiB | 355.4 | 355.5 | 355.8 | 356.4 | 357.5 | 359.8 |
| Q4_0 | 353.48 GiB | 354.5 | 354.6 | 354.9 | 355.5 | 356.6 | 358.9 |
| IQ4_NL | 352.58 GiB | 353.6 | 353.7 | 354.0 | 354.6 | 355.7 | 358.0 |
| IQ4_XS | 333.14 GiB | 334.2 | 334.3 | 334.6 | 335.2 | 336.3 | 338.6 |
| Q3_K_M | 297.88 GiB | 298.9 | 299.0 | 299.3 | 299.9 | 301.0 | 303.3 |
| Q3_K_S | 269.83 GiB | 270.9 | 271.0 | 271.3 | 271.9 | 273.0 | 275.3 |
| UD-IQ3_XXS | 254.17 GiB | 255.2 | 255.3 | 255.6 | 256.2 | 257.3 | 259.6 |
| Q2_K_L | 228.17 GiB | 229.2 | 229.3 | 229.6 | 230.2 | 231.3 | 233.6 |
| Q2_K | 227.97 GiB | 229.0 | 229.1 | 229.4 | 230.0 | 231.1 | 233.4 |
| UD-IQ2_M | 212.30 GiB | 213.3 | 213.5 | 213.8 | 214.3 | 215.5 | 217.7 |
| UD-IQ2_XXS | 201.37 GiB | 202.4 | 202.5 | 202.8 | 203.4 | 204.5 | 206.8 |
| UD-IQ1_M | 186.69 GiB | 187.7 | 187.9 | 188.1 | 188.7 | 189.8 | 192.1 |
| UD-IQ1_S | 172.39 GiB | 173.4 | 173.6 | 173.8 | 174.4 | 175.5 | 177.8 |
| UD-TQ1_0 | 150.08 GiB | 151.1 | 151.2 | 151.5 | 152.1 | 153.2 | 155.5 |
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 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, 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.