Can I Run Llama 3 8B Instruct on a Apple M3 (8GB)?
Runs comfortably at Q6_K — minimal quality loss.
13 quantizations fit your 8.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q6_KBEST | 7.6 GB | 9.1 GB | 6.6 GB | +0.4 GB |
| Q5_1 | 7.0 GB | 8.5 GB | 6.1 GB | +1.0 GB |
| Q5_K_M | 6.7 GB | 8.2 GB | 5.7 GB | +1.3 GB |
| Q5_K_S | 6.5 GB | 8.0 GB | 5.6 GB | +1.5 GB |
| Q5_0 | 6.5 GB | 8.0 GB | 5.6 GB | +1.5 GB |
| Q4_1 | 6.0 GB | 7.5 GB | 5.1 GB | +2.0 GB |
| Q4_K_M | 5.8 GB | 7.3 GB | 4.9 GB | +2.2 GB |
| Q4_K_S | 5.6 GB | 7.1 GB | 4.7 GB | +2.4 GB |
| Q4_0 | 5.5 GB | 7.0 GB | 4.7 GB | +2.5 GB |
| Q3_K_L | 4.6 GB | 6.1 GB | 4.3 GB | +3.4 GB |
| Q3_K_M | 4.3 GB | 5.8 GB | 4.0 GB | +3.7 GB |
| Q3_K_S | 4.1 GB | 5.6 GB | 3.7 GB | +3.9 GB |
| Q2_K | 3.6 GB | 5.1 GB | 3.2 GB | +4.4 GB |
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All quant variants, benchmark scores, and use-case tags.
Top-ranked open-source models that fit in 8.0GB.
FAQ
Can the Apple M3 (8GB) run Llama 3 8B Instruct?
Yes. The Apple M3 (8GB)'s 8.0GB of unified memory is enough to run Llama 3 8B Instruct at Q6_K quantization (7.6GB required).
What's the best quantization to use?
Q6_K is the highest-precision quantization that fits in your 8.0GB. It uses about 7.6GB of memory and 9.1GB 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.