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No. Gemma 4 12B does not fit on M3 8GB: Q4_K_M needs 9.6 GB and 5.4 GB is usable.
Hardware: M3 8GB · Model: Gemma 4 12B
M3 8GB has 8 GB of unified memory and 100 GB/s of memory bandwidth. M2 8GB has the same memory and bandwidth, so the fit numbers match; this page is M3 8GB. The checker leaves macOS a share of unified memory, so Ollama can use 5.4 GB. Gemma 4 12B (12B, Google) at Q4_K_M is a 8 GB download and needs 9.6 GB with headroom for about 8k tokens.
| Best quant | Q4_K_M |
|---|---|
| Download size | 8 GB |
| Memory needed | 9.6 GB |
| Usable fast memory | 5.4 GB |
| Fit | does not fit |
| Estimated speed | no estimate, it does not fit |
| Command | ollama run gemma4:12b-it-q4_K_M |
Smallest hardware that runs it: RTX 3060 8GB at Q4_K_M (partial offload (slower), 9.6 GB needed, 7.5 GB usable, about 11 tok/s (estimate)). Can a RTX 3060 8GB run Gemma 4 12B?
A smaller Google model that fits this hardware: Gemma 3 4B at Q4_K_M (fits in fast memory, 4.6 GB). Can a M3 8GB run Gemma 3 4B?
Open M3 8GB in the checkerEstimates, not benchmarks. Model sizes are ollama.com download sizes; memory needed adds headroom for ~8k tokens of context; speeds are rough figures from memory bandwidth. Data updated 2026-10-23.
A project by OnDevice Labs · llms.txt