Can I Run gemma 4 E2B it on a Apple M2 (16GB)?
Runs at full precision (fp16). Zero quality loss.
11 quantizations fit your 16.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 11.2 GB | 12.7 GB | 1.0 GB | +4.8 GB |
| Q8_0 | 6.4 GB | 7.9 GB | 5.0 GB | +9.6 GB |
| Q6_K | 5.2 GB | 6.7 GB | 4.5 GB | +10.8 GB |
| Q5_K_M | 4.6 GB | 6.1 GB | 3.4 GB | +11.4 GB |
| Q5_K_S | 4.5 GB | 6.0 GB | 3.3 GB | +11.5 GB |
| Q4_1 | 4.2 GB | 5.7 GB | 3.1 GB | +11.8 GB |
| Q4_K_M | 4.1 GB | 5.6 GB | 3.1 GB | +11.9 GB |
| Q4_K_S | 3.9 GB | 5.4 GB | 3.0 GB | +12.1 GB |
| Q4_0 | 3.9 GB | 5.4 GB | 3.0 GB | +12.1 GB |
| Q3_K_M | 3.1 GB | 4.6 GB | 2.5 GB | +12.9 GB |
| Q3_K_S | 3.0 GB | 4.5 GB | 2.5 GB | +13.0 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 (16GB) run gemma 4 E2B it?
Yes. The Apple M2 (16GB)'s 16.0GB of unified memory is enough to run gemma 4 E2B it at fp16 quantization (11.2GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 16.0GB. It uses about 11.2GB of memory and 12.7GB 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.