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Yes. Gemma 3n E2B (on-device) fits in fast memory on M3 24GB at Q8_0 (7.8 GB needed, 16.1 GB usable), about 12 tok/s (estimate).
Hardware: M3 24GB · Model: Gemma 3n E2B (on-device)
M3 24GB has 24 GB of unified memory and 100 GB/s of memory bandwidth. M2 24GB has the same memory and bandwidth, so the fit numbers match; this page is M3 24GB. The checker leaves macOS a share of unified memory, so Ollama can use 16.1 GB. Gemma 3n E2B (on-device) (5.4B, Google) at Q8_0 is a 6.9 GB download and needs 7.8 GB with headroom for about 8k tokens.
| Best quant | Q8_0 |
|---|---|
| Download size | 6.9 GB |
| Memory needed | 7.8 GB |
| Usable fast memory | 16.1 GB |
| Fit | fits in fast memory |
| Estimated speed | ~12 tok/s (estimate) |
| Command | ollama run gemma3n:e2b-it-q8_0 |
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