Can I Run gemma 4 E4B it on a Apple M3 Max (36GB)?
Runs at full precision (f32). Zero quality loss.
12 quantizations fit your 36.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| f32BEST | 33.0 GB | 34.5 GB | 1.9 GB | +3.0 GB |
| fp16 | 17.0 GB | 18.5 GB | 1.0 GB | +19.0 GB |
| Q8_0 | 9.5 GB | 11.0 GB | 8.2 GB | +26.5 GB |
| Q6_K | 7.6 GB | 9.1 GB | 7.1 GB | +28.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.5 GB | +29.3 GB |
| Q5_K_S | 6.5 GB | 8.0 GB | 5.4 GB | +29.5 GB |
| Q4_1 | 6.0 GB | 7.5 GB | 5.1 GB | +30.0 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 5.0 GB | +30.1 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.8 GB | +30.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.8 GB | +30.5 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.1 GB | +31.6 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.9 GB | +31.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 36.0GB.
FAQ
Can the Apple M3 Max (36GB) run gemma 4 E4B it?
Yes. The Apple M3 Max (36GB)'s 36.0GB of unified memory is enough to run gemma 4 E4B it at f32 quantization (33.0GB required).
What's the best quantization to use?
f32 is the highest-precision quantization that fits in your 36.0GB. It uses about 33.0GB of memory and 34.5GB 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.