gemma-3-12b-it-qat requirements — can you run it?

Yes, if combined RAM+VRAM ≥ file size — but generation speed is limited by memory bandwidth. Expect roughly: DDR5 dual channel ~10-30 tok/s for small MoE actives, single digits for big ones, 0.1-1 tok/s when experts page from disk. CPU-only is fine for batch jobs, painful for chat.

Most pages compute weights from a formula (params × bits/8). We use the actual file sizes published in GGUF repos, which include the embedding table, unquantized tensors, and container overhead — that is why our numbers can differ from a naive calculation.

The measured answer: 7.8 GB at the UD-IQ2_XXS quant with 8K context. The smallest published build needs 7.3 GB; the lossless (F16/BF16) build needs 28.6 GB. These are real GGUF file sizes from Hugging Face, not formula estimates.

Yes — at UD-IQ2_XXS (7.8 GB). Quants that fit 24GB: UD-IQ1_S, UD-IQ1_M, UD-IQ2_XXS, UD-IQ2_M, Q2_K, Q2_K_L, UD-IQ3_XXS, UD-Q2_K_XL, Q3_K_S, Q3_K_M, UD-Q3_K_XL, IQ4_XS, IQ4_NL, Q4_0, Q4_K_S, Q4_K_M, UD-Q4_K_XL, Q4_1, Q5_K_S, Q5_K_M, UD-Q5_K_XL, Q6_K, UD-Q6_K_XL, Q8_0, UD-Q8_K_XL.

Yes, if your usable RAM is at least the file size. At UD-IQ2_XXS you need ~7.8 GB of RAM. CPU generation is memory-bandwidth-bound: expect single-digit tok/s for large MoE models, 10-30 tok/s for small active params, and below 1 tok/s when experts page from disk.

Most sites compute weights from a formula (params x bits/8). ModelFit uses the actual GGUF file sizes published on Hugging Face, which include the embedding table, unquantized tensors, and container overhead — so our numbers reflect what you really download and load.