← CanIRunLocal · All hardware · All models
Yes. d1-omni 600M (Liquid AI, decision) fits in fast memory on M2 Pro 16GB at Q8_0 (1.8 GB needed, 10.7 GB usable), about 188 tok/s (estimate).
Hardware: M2 Pro 16GB · Model: d1-omni 600M (Liquid AI, decision)
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. d1-omni 600M (Liquid AI, decision) (0.587B, Liquid AI) at Q8_0 is a 0.85 GB download and needs 1.8 GB with headroom for about 8k tokens.
| Best quant | Q8_0 |
|---|---|
| Download size | 0.85 GB |
| Memory needed | 1.8 GB |
| Usable fast memory | 10.7 GB |
| Fit | fits in fast memory |
| Estimated speed | ~188 tok/s (estimate) |
| Command | ollama run d1-omni:600m |
Estimates, 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