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No. Gemma 4 31B does not fit on M1 Pro 16GB: Q4_K_M needs 22.5 GB and 10.7 GB is usable.
Hardware: M1 Pro 16GB · Model: Gemma 4 31B
M1 Pro 16GB has 16 GB of unified memory and 200 GB/s of memory bandwidth. M2 Pro 16GB has the same memory and bandwidth, so the fit numbers match; this page is M1 Pro 16GB. The checker leaves macOS a share of unified memory, so Ollama can use 10.7 GB. Gemma 4 31B (31.3B, Google) at Q4_K_M is a 20 GB download and needs 22.5 GB with headroom for about 8k tokens.
| Best quant | Q4_K_M |
|---|---|
| Download size | 20 GB |
| Memory needed | 22.5 GB |
| Usable fast memory | 10.7 GB |
| Fit | does not fit |
| Estimated speed | no estimate, it does not fit |
| Command | ollama run gemma4:31b-it-q4_K_M |
Smallest hardware that runs it: RTX 4060 Ti 16GB at Q4_K_M (partial offload (slower), 22.5 GB needed, 15.5 GB usable, about 4 tok/s (estimate)). Can a RTX 4060 Ti 16GB run Gemma 4 31B?
A smaller Google model that fits this hardware: Gemma 3 12B at Q4_K_M (fits in fast memory, 9.8 GB). Can a M1 Pro 16GB run Gemma 3 12B?
Open M1 Pro 16GB 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