Can I Run gemma 4 E4B it on a Apple M5 (32GB)?
Runs at full precision (fp16). Zero quality loss.
11 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 17.0 GB | 18.5 GB | 1.0 GB | +15.0 GB |
| Q8_0 | 9.5 GB | 11.0 GB | 8.2 GB | +22.5 GB |
| Q6_K | 7.6 GB | 9.1 GB | 7.1 GB | +24.4 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.5 GB | +25.3 GB |
| Q5_K_S | 6.5 GB | 8.0 GB | 5.4 GB | +25.5 GB |
| Q4_1 | 6.0 GB | 7.5 GB | 5.1 GB | +26.0 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 5.0 GB | +26.1 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.8 GB | +26.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.8 GB | +26.5 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.1 GB | +27.6 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.9 GB | +27.9 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the Apple M5 (32GB) run gemma 4 E4B it?
Yes. The Apple M5 (32GB)'s 32.0GB of unified memory is enough to run gemma 4 E4B it at fp16 quantization (17.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 32.0GB. It uses about 17.0GB of memory and 18.5GB 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.