Can I Run Gemma 4 E2B on a Apple M2 Pro (16GB)?
Runs at full precision (f32). Zero quality loss.
14 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 9.0 GB | 10.5 GB | 1.9 GB | +7.0 GB |
| fp16 | 5.0 GB | 6.5 GB | 0.2 GB | +11.0 GB |
| Q8_0 | 3.1 GB | 4.6 GB | 0.1 GB | +12.9 GB |
| Q6_K | 2.6 GB | 4.2 GB | 4.5 GB | +13.3 GB |
| Q5_K_M | 2.4 GB | 3.9 GB | 3.4 GB | +13.6 GB |
| Q5_K_S | 2.4 GB | 3.9 GB | 3.3 GB | +13.6 GB |
| Q4_1 | 2.3 GB | 3.8 GB | 3.1 GB | +13.8 GB |
| Q4_K_M | 2.2 GB | 3.7 GB | 3.1 GB | +13.8 GB |
| Q4_K_S | 2.1 GB | 3.6 GB | 3.0 GB | +13.8 GB |
| Q4_0 | 2.1 GB | 3.6 GB | 3.0 GB | +13.9 GB |
| Q3_K_L | 1.9 GB | 3.4 GB | 3.3 GB | +14.1 GB |
| Q3_K_M | 1.8 GB | 3.3 GB | 2.5 GB | +14.2 GB |
| Q3_K_S | 1.8 GB | 3.3 GB | 2.5 GB | +14.2 GB |
| Q2_K | 1.7 GB | 3.2 GB | 3.0 GB | +14.3 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 16.0GB.
FAQ
Can the Apple M2 Pro (16GB) run Gemma 4 E2B?
Yes. The Apple M2 Pro (16GB)'s 16.0GB of unified memory is enough to run Gemma 4 E2B at f32 quantization (9.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 16.0GB. It uses about 9.0GB of memory and 10.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.