Does Kimi-K2.6 fit in 24GB of VRAM?
Not at these settings. No indexed quantization of Kimi-K2.6 fits 24GB card at any context we compute, with q4_0 KV. The smallest shipped quantization is 193.16 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 | 1912.15 GiB | 1913.1 | 1913.2 | 1913.3 | 1913.6 | 1914.2 | 1915.4 |
| Q4_0 | 543.62 GiB | 544.6 | 544.7 | 544.8 | 545.1 | 545.7 | 546.9 |
| IQ3_M | 454.53 GiB | 455.5 | 455.6 | 455.7 | 456.0 | 456.6 | 457.8 |
| Q3_K_L | 454.05 GiB | 455.0 | 455.1 | 455.2 | 455.5 | 456.1 | 457.3 |
| Q3_K_M | 434.81 GiB | 435.8 | 435.8 | 436.0 | 436.3 | 436.9 | 438.1 |
| IQ3_XS | 434.41 GiB | 435.4 | 435.4 | 435.6 | 435.9 | 436.5 | 437.7 |
| Q3_K_S | 413.86 GiB | 414.8 | 414.9 | 415.0 | 415.3 | 415.9 | 417.2 |
| IQ3_XXS | 397.34 GiB | 398.3 | 398.4 | 398.5 | 398.8 | 399.4 | 400.6 |
| Q2_K_L | 334.66 GiB | 335.6 | 335.7 | 335.8 | 336.1 | 336.7 | 338.0 |
| Q2_K | 333.59 GiB | 334.5 | 334.6 | 334.8 | 335.1 | 335.7 | 336.9 |
| IQ2_M | 318.27 GiB | 319.2 | 319.3 | 319.5 | 319.8 | 320.4 | 321.6 |
| IQ2_S | 287.58 GiB | 288.5 | 288.6 | 288.8 | 289.1 | 289.7 | 290.9 |
| IQ2_XS | 282.35 GiB | 283.3 | 283.4 | 283.5 | 283.8 | 284.4 | 285.6 |
| IQ2_XXS | 252.81 GiB | 253.8 | 253.8 | 254.0 | 254.3 | 254.9 | 256.1 |
| IQ1_M | 216.46 GiB | 217.4 | 217.5 | 217.6 | 217.9 | 218.5 | 219.8 |
| IQ1_S | 193.16 GiB | 194.1 | 194.2 | 194.3 | 194.6 | 195.2 | 196.5 |
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 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, 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.