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No. Llama 4 Scout 109B (MoE) does not fit on M2 Pro 32GB: Q4_K_M needs 69.7 GB and 24.0 GB is usable.
Hardware: M2 Pro 32GB · Model: Llama 4 Scout 109B (MoE)
M2 Pro 32GB has 32 GB of unified memory and 200 GB/s of memory bandwidth. M1 Pro 32GB has the same memory and bandwidth, so the fit numbers match; this page is M2 Pro 32GB. The checker leaves macOS a share of unified memory, so Ollama can use 24.0 GB. Llama 4 Scout 109B (MoE) (109B, 17B active, Meta) at Q4_K_M is a 67 GB download and needs 69.7 GB with headroom for about 8k tokens.
| Best quant | Q4_K_M |
|---|---|
| Download size | 67 GB |
| Memory needed | 69.7 GB |
| Usable fast memory | 24.0 GB |
| Fit | does not fit |
| Estimated speed | no estimate, it does not fit |
| Command | ollama run llama4:16x17b |
Smallest hardware that runs it: M2 Max (38c GPU) 96GB at Q4_K_M (fits in fast memory, 69.7 GB needed, 72.0 GB usable, about 15 tok/s (estimate)). Can a M2 Max (38c GPU) 96GB run Llama 4 Scout 109B (MoE)?
A smaller Meta model that fits this hardware: Llama 3.2 Vision 11B at Q8_0 (fits in fast memory, 13.5 GB). Can a M2 Pro 32GB run Llama 3.2 Vision 11B?
Open M2 Pro 32GB 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