google_gemma-3-4b-it 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: 3.3 GB at the IQ2_M quant with 8K context. The smallest published build needs 3.3 GB; the lossless (F16/BF16) build needs 6.5 GB. These are real GGUF file sizes from Hugging Face, not formula estimates.

Yes — at IQ2_M (3.3 GB). Quants that fit 24GB: IQ2_M, IQ3_XXS, Q2_K, IQ3_XS, Q2_K_L, Q3_K_S, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, IQ4_NL, Q4_0, Q4_K_S, Q3_K_XL, Q4_K_M, Q4_1, Q4_K_L, Q5_K_S, Q5_K_M, Q5_K_L, Q6_K, Q6_K_L, Q8_0.

Yes, if your usable RAM is at least the file size. At IQ2_M you need ~3.3 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.