Does GLM-4.7-Flash-heretic fit in 8GB of VRAM?
Not at these settings. No indexed quantization of GLM-4.7-Flash-heretic fits 8GB card at any context we compute, with q8_0 KV. The smallest shipped quantization is 9.61 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◐ |
|---|---|---|---|---|---|---|---|
| Q8_0 | 29.93 GiB | 30.9 | 31.0 | 31.2 | 31.6 | 32.5 | 34.3 |
| Q5_1 | 29.75 GiB | 30.7 | 30.8 | 31.0 | 31.4 | 32.3 | 34.1 |
| Q4_K_M | 24.06 GiB | 25.0 | 25.1 | 25.3 | 25.8 | 26.6 | 28.4 |
| Q6_K | 23.27 GiB | 24.2 | 24.3 | 24.5 | 25.0 | 25.8 | 27.6 |
| Q5_K_M | 20.15 GiB | 21.1 | 21.2 | 21.4 | 21.8 | 22.7 | 24.5 |
| Q5_K_S | 19.59 GiB | 20.5 | 20.6 | 20.8 | 21.3 | 22.2 | 23.9 |
| Q4_1 | 17.86 GiB | 18.8 | 18.9 | 19.1 | 19.6 | 20.4 | 22.2 |
| Q4_K_S | 16.25 GiB | 17.2 | 17.3 | 17.5 | 17.9 | 18.8 | 20.6 |
| IQ4_NL | 16.14 GiB | 17.1 | 17.2 | 17.4 | 17.8 | 18.7 | 20.5 |
| IQ4_XS | 15.28 GiB | 16.2 | 16.3 | 16.5 | 17.0 | 17.8 | 19.6 |
| IQ3_M | 12.65 GiB | 13.6 | 13.7 | 13.9 | 14.3 | 15.2 | 17.0 |
| IQ2_M | 9.61 GiB | 10.5 | 10.6 | 10.9 | 11.3 | 12.2 | 13.9 |
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.