Does Kimi-K2.5 fit in 8GB of VRAM?
Not at these settings. No indexed quantization of Kimi-K2.5 fits 8GB card at any context we compute, with q8_0 KV. The smallest shipped quantization is 195.86 GiB in weights alone, against 7.44 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 | 1913.2 | 1913.3 | 1913.6 | 1914.2 | 1915.3 | 1917.6 |
| Q6_K | 785.02 GiB | 786.0 | 786.2 | 786.5 | 787.0 | 788.2 | 790.5 |
| Q5_K_M | 678.69 GiB | 679.7 | 679.9 | 680.1 | 680.7 | 681.8 | 684.1 |
| Q5_K_S | 658.45 GiB | 659.5 | 659.6 | 659.9 | 660.5 | 661.6 | 663.9 |
| Q4_1 | 598.99 GiB | 600.0 | 600.2 | 600.4 | 601.0 | 602.1 | 604.4 |
| Q4_K_M | 578.58 GiB | 579.6 | 579.7 | 580.0 | 580.6 | 581.7 | 584.0 |
| Q4_0 | 549.26 GiB | 550.3 | 550.4 | 550.7 | 551.3 | 552.4 | 554.7 |
| Q8_0 | 543.62 GiB | 544.6 | 544.8 | 545.1 | 545.6 | 546.8 | 549.1 |
| Q4_K_S | 543.24 GiB | 544.3 | 544.4 | 544.7 | 545.3 | 546.4 | 548.7 |
| Q4_K_L | 541.31 GiB | 542.3 | 542.5 | 542.8 | 543.3 | 544.5 | 546.7 |
| IQ4_NL | 539.67 GiB | 540.7 | 540.8 | 541.1 | 541.7 | 542.8 | 545.1 |
| IQ4_XS | 510.00 GiB | 511.0 | 511.2 | 511.4 | 512.0 | 513.2 | 515.4 |
| Q3_K_M | 456.14 GiB | 457.2 | 457.3 | 457.6 | 458.2 | 459.3 | 461.6 |
| Q3_K_L | 454.05 GiB | 455.1 | 455.2 | 455.5 | 456.1 | 457.2 | 459.5 |
| IQ3_M | 434.81 GiB | 435.8 | 436.0 | 436.3 | 436.8 | 438.0 | 440.2 |
| Q3_K_S | 414.28 GiB | 415.3 | 415.4 | 415.7 | 416.3 | 417.4 | 419.7 |
| IQ3_XS | 391.23 GiB | 392.3 | 392.4 | 392.7 | 393.3 | 394.4 | 396.7 |
| UD-IQ3_XXS | 386.32 GiB | 387.3 | 387.5 | 387.8 | 388.3 | 389.5 | 391.8 |
| IQ3_S | 377.51 GiB | 378.5 | 378.7 | 379.0 | 379.5 | 380.7 | 382.9 |
| IQ3_XXS | 376.80 GiB | 377.8 | 378.0 | 378.2 | 378.8 | 380.0 | 382.2 |
| Q2_K_L | 348.36 GiB | 349.4 | 349.5 | 349.8 | 350.4 | 351.5 | 353.8 |
| Q2_K | 348.11 GiB | 349.1 | 349.3 | 349.6 | 350.1 | 351.3 | 353.5 |
| UD-IQ2_M | 321.54 GiB | 322.6 | 322.7 | 323.0 | 323.6 | 324.7 | 327.0 |
| IQ2_S | 311.72 GiB | 312.7 | 312.9 | 313.2 | 313.7 | 314.9 | 317.2 |
| UD-IQ2_XXS | 304.30 GiB | 305.3 | 305.5 | 305.7 | 306.3 | 307.5 | 309.7 |
| IQ2_M | 300.77 GiB | 301.8 | 301.9 | 302.2 | 302.8 | 303.9 | 306.2 |
| UD-IQ1_M | 279.94 GiB | 281.0 | 281.1 | 281.4 | 282.0 | 283.1 | 285.4 |
| IQ2_XS | 263.67 GiB | 264.7 | 264.8 | 265.1 | 265.7 | 266.8 | 269.1 |
| IQ2_XXS | 262.75 GiB | 263.8 | 263.9 | 264.2 | 264.8 | 265.9 | 268.2 |
| UD-IQ1_S | 256.97 GiB | 258.0 | 258.1 | 258.4 | 259.0 | 260.1 | 262.4 |
| UD-TQ1_0 | 223.09 GiB | 224.1 | 224.3 | 224.5 | 225.1 | 226.2 | 228.5 |
| IQ1_M | 204.50 GiB | 205.5 | 205.7 | 206.0 | 206.5 | 207.7 | 209.9 |
| IQ1_S | 195.86 GiB | 196.9 | 197.0 | 197.3 | 197.9 | 199.0 | 201.3 |
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 8GB 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.