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No. LFM2.5 24B-A2B (MoE) does not fit on M2 Pro 16GB: Q4_K_M needs 16.2 GB and 10.7 GB is usable.
Hardware: M2 Pro 16GB · Model: LFM2.5 24B-A2B (MoE)
M2 Pro 16GB has 16 GB of unified memory and 200 GB/s of memory bandwidth. M1 Pro 16GB has the same memory and bandwidth, so the fit numbers match; this page is M2 Pro 16GB. The checker leaves macOS a share of unified memory, so Ollama can use 10.7 GB. LFM2.5 24B-A2B (MoE) (24B, 2B active, Liquid AI) at Q4_K_M is a 15 GB download and needs 16.2 GB with headroom for about 8k tokens.
| Best quant | Q4_K_M |
|---|---|
| Download size | 15 GB |
| Memory needed | 16.2 GB |
| Usable fast memory | 10.7 GB |
| Fit | does not fit |
| Estimated speed | no estimate, it does not fit |
| Command | ollama run lfm2.5:24b |
Smallest hardware that runs it: RTX 3060 8GB at Q4_K_M (partial offload (slower), 16.2 GB needed, 7.5 GB usable, about 24 tok/s (estimate)). Can a RTX 3060 8GB run LFM2.5 24B-A2B (MoE)?
A smaller Liquid AI model that fits this hardware: LFM2.5 8B-A1B (MoE) at Q8_0 (fits in fast memory, 9.8 GB). Can a M2 Pro 16GB run LFM2.5 8B-A1B (MoE)?
Open M2 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.
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