Does GLM-5.1 fit in 24GB of VRAM?
Not at these settings. No indexed quantization of GLM-5.1 fits 24GB card at any context we compute, with q4_0 KV. The smallest shipped quantization is 147.30 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 | 1404.42 GiB | 1405.4 | 1405.5 | 1405.6 | 1406.0 | 1406.8 | 1408.3 |
| Q8_0 | 746.31 GiB | 747.3 | 747.4 | 747.5 | 747.9 | 748.7 | 750.2 |
| Q6_K | 607.66 GiB | 608.6 | 608.7 | 608.9 | 609.3 | 610.1 | 611.6 |
| UD-Q6_K | 578.66 GiB | 579.6 | 579.7 | 579.9 | 580.3 | 581.1 | 582.6 |
| UD-Q5_K_M | 520.11 GiB | 521.1 | 521.1 | 521.3 | 521.7 | 522.5 | 524.0 |
| Q5_K_M | 501.62 GiB | 502.6 | 502.7 | 502.9 | 503.2 | 504.0 | 505.6 |
| UD-Q5_K_S | 489.83 GiB | 490.8 | 490.9 | 491.1 | 491.4 | 492.2 | 493.8 |
| Q5_K_S | 484.36 GiB | 485.3 | 485.4 | 485.6 | 486.0 | 486.8 | 488.3 |
| Q4_1 | 440.26 GiB | 441.2 | 441.3 | 441.5 | 441.9 | 442.6 | 444.2 |
| UD-Q4_K_M | 432.60 GiB | 433.5 | 433.6 | 433.8 | 434.2 | 435.0 | 436.5 |
| Q4_K_L | 428.77 GiB | 429.7 | 429.8 | 430.0 | 430.4 | 431.2 | 432.7 |
| Q4_K_M | 428.11 GiB | 429.1 | 429.1 | 429.3 | 429.7 | 430.5 | 432.0 |
| Q4_K_S | 411.10 GiB | 412.0 | 412.1 | 412.3 | 412.7 | 413.5 | 415.0 |
| UD-Q4_K_S | 404.10 GiB | 405.0 | 405.1 | 405.3 | 405.7 | 406.5 | 408.0 |
| Q4_0 | 398.24 GiB | 399.2 | 399.3 | 399.5 | 399.9 | 400.6 | 402.2 |
| IQ4_NL | 397.30 GiB | 398.2 | 398.3 | 398.5 | 398.9 | 399.7 | 401.2 |
| IQ4_XS | 375.69 GiB | 376.6 | 376.7 | 376.9 | 377.3 | 378.1 | 379.6 |
| UD-IQ4_NL | 343.54 GiB | 344.5 | 344.6 | 344.8 | 345.2 | 345.9 | 347.5 |
| UD-IQ4_XS | 336.51 GiB | 337.5 | 337.6 | 337.7 | 338.1 | 338.9 | 340.4 |
| IQ3_M | 335.52 GiB | 336.5 | 336.6 | 336.8 | 337.1 | 337.9 | 339.5 |
| Q3_K_L | 334.23 GiB | 335.2 | 335.3 | 335.5 | 335.8 | 336.6 | 338.2 |
| Q3_K_M | 320.96 GiB | 321.9 | 322.0 | 322.2 | 322.6 | 323.4 | 324.9 |
| IQ3_XS | 320.32 GiB | 321.3 | 321.4 | 321.6 | 321.9 | 322.7 | 324.2 |
| UD-Q3_K_M | 315.19 GiB | 316.1 | 316.2 | 316.4 | 316.8 | 317.6 | 319.1 |
| Q3_K_S | 305.77 GiB | 306.7 | 306.8 | 307.0 | 307.4 | 308.2 | 309.7 |
| IQ3_XXS | 293.34 GiB | 294.3 | 294.4 | 294.6 | 295.0 | 295.7 | 297.3 |
| UD-Q3_K_S | 291.94 GiB | 292.9 | 293.0 | 293.2 | 293.6 | 294.3 | 295.9 |
| UD-IQ3_S | 260.39 GiB | 261.3 | 261.4 | 261.6 | 262.0 | 262.8 | 264.3 |
| UD-IQ3_XXS | 249.84 GiB | 250.8 | 250.9 | 251.1 | 251.5 | 252.2 | 253.8 |
| Q2_K_L | 248.43 GiB | 249.4 | 249.5 | 249.7 | 250.1 | 250.8 | 252.4 |
| Q2_K | 247.56 GiB | 248.5 | 248.6 | 248.8 | 249.2 | 250.0 | 251.5 |
| IQ2_M | 237.40 GiB | 238.3 | 238.4 | 238.6 | 239.0 | 239.8 | 241.3 |
| UD-IQ2_M | 219.96 GiB | 220.9 | 221.0 | 221.2 | 221.6 | 222.4 | 223.9 |
| IQ2_S | 215.49 GiB | 216.4 | 216.5 | 216.7 | 217.1 | 217.9 | 219.4 |
| IQ2_XS | 211.04 GiB | 212.0 | 212.1 | 212.3 | 212.7 | 213.4 | 215.0 |
| UD-IQ2_XXS | 205.49 GiB | 206.4 | 206.5 | 206.7 | 207.1 | 207.9 | 209.4 |
| UD-IQ1_M | 191.42 GiB | 192.4 | 192.5 | 192.7 | 193.0 | 193.8 | 195.4 |
| IQ2_XXS | 189.92 GiB | 190.9 | 191.0 | 191.2 | 191.5 | 192.3 | 193.9 |
| IQ1_M | 163.93 GiB | 164.9 | 165.0 | 165.2 | 165.6 | 166.3 | 167.9 |
| IQ1_S | 147.30 GiB | 148.2 | 148.3 | 148.5 | 148.9 | 149.7 | 151.2 |
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.