Can I Run / Gemma 4 E2B / on Apple M1 (8GB)
Can I Run Gemma 4 E2B on a Apple M1 (8GB)?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
2.0B
GPU memory
8.0GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 5.0 GB | 6.5 GB | 4.0 GB | +3.0 GB |
| Q8_0 | 3.1 GB | 4.6 GB | 2.1 GB | +4.9 GB |
| Q6_K | 2.6 GB | 4.2 GB | 1.6 GB | +5.3 GB |
| Q5_K_M | 2.4 GB | 3.9 GB | 1.4 GB | +5.6 GB |
| Q4_K_M | 2.2 GB | 3.7 GB | 1.2 GB | +5.8 GB |
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Full model details
Gemma 4 E2B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
Apple M1 (8GB) →
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the Apple M1 (8GB) run Gemma 4 E2B?
Yes. The Apple M1 (8GB)'s 8.0GB of unified memory is enough to run Gemma 4 E2B at fp16 quantization (5.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 8.0GB. It uses about 5.0GB of memory and 6.5GB 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.