Can I Run / gemma 4 E2B it / on Apple M1 (8GB)
Can I Run gemma 4 E2B it on a Apple M1 (8GB)?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
5.1B
GPU memory
8.0GB
Smallest quant
Q3_K_S
Best fit
Q8_0
10 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 6.4 GB | 7.9 GB | 5.0 GB | +1.6 GB |
| Q6_K | 5.2 GB | 6.7 GB | 4.5 GB | +2.8 GB |
| Q5_K_M | 4.6 GB | 6.1 GB | 3.4 GB | +3.4 GB |
| Q5_K_S | 4.5 GB | 6.0 GB | 3.3 GB | +3.5 GB |
| Q4_1 | 4.2 GB | 5.7 GB | 3.1 GB | +3.8 GB |
| Q4_K_M | 4.1 GB | 5.6 GB | 3.1 GB | +3.9 GB |
| Q4_K_S | 3.9 GB | 5.4 GB | 3.0 GB | +4.1 GB |
| Q4_0 | 3.9 GB | 5.4 GB | 3.0 GB | +4.1 GB |
| Q3_K_M | 3.1 GB | 4.6 GB | 2.5 GB | +4.9 GB |
| Q3_K_S | 3.0 GB | 4.5 GB | 2.5 GB | +5.0 GB |
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Full model details
gemma 4 E2B it →
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 it?
Yes. The Apple M1 (8GB)'s 8.0GB of unified memory is enough to run gemma 4 E2B it at Q8_0 quantization (6.4GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 8.0GB. It uses about 6.4GB of memory and 7.9GB 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.