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Yes. Gemma 4 E2B (effective 2.3B) fits in fast memory on Mac Studio M3 Ultra (80c GPU) 96GB at Q8_0 (7.2 GB needed, 72.0 GB usable), about 109 tok/s (estimate).
Hardware: Mac Studio M3 Ultra (80c GPU) 96GB · Model: Gemma 4 E2B (effective 2.3B)
Mac Studio M3 Ultra (80c GPU) 96GB has 96 GB of unified memory and 819 GB/s of memory bandwidth. Mac Studio M3 Ultra (60c GPU) 96GB has the same memory and bandwidth, so the fit numbers match; this page is Mac Studio M3 Ultra (80c GPU) 96GB. The checker leaves macOS a share of unified memory, so Ollama can use 72.0 GB. Gemma 4 E2B (effective 2.3B) (5.12B, Google) at Q8_0 is a 6 GB download and needs 7.2 GB with headroom for about 8k tokens.
| Best quant | Q8_0 |
|---|---|
| Download size | 6 GB |
| Memory needed | 7.2 GB |
| Usable fast memory | 72.0 GB |
| Fit | fits in fast memory |
| Estimated speed | ~109 tok/s (estimate) |
| Command | ollama run gemma4: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.
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