Can I Run SmolLM3 on Mac mini (M4 Pro, 64 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 6 września 2026
Yes — comfortably
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~43.1 GB spare and running at ~36.1 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~36.1 tok/s
See what else this hardware can run →
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Mac mini (M4 Pro, 64 GB) — what it gives a model
| Usable memory for models | 48 GB |
| Memory bandwidth | 273 GB/s |
| Form factor | Mini PC |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
SmolLM3 on Mac mini (M4 Pro, 64 GB): memory by quantization
| Quant | Memory needed | Fits 48 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 7.8 GB | ✓ Yes | 128K | ~21.3 tok/s | 6.2 GB |
| Q8_0 | 4.9 GB | ✓ Yes | 128K | ~36.1 tok/s | 3.3 GB |
| Q6_K | 4.2 GB | ✓ Yes | 128K | ~44.1 tok/s | 2.5 GB |
| Q5_K_M | 3.8 GB | ✓ Yes | 128K | ~49 tok/s | 2.2 GB |
| Q4_K_M | 3.5 GB | ✓ Yes | 128K | ~54.9 tok/s | 1.9 GB |
| Q3_K_M | 3 GB | ✓ Yes | 128K | ~68.6 tok/s | 1.3 GB |
| Q2_K | 2.7 GB | ✓ Yes | 128K | ~79.6 tok/s | 1 GB |
What to watch out for
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
- Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.
Mac mini M4 Pro 64 GB limitations
- M4 Pro memory bandwidth is half the M4 Max's, so large models that fit will still generate roughly half as fast.
Recommended setup
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 64 GB unified memory at 273 GB/s, shared between CPU and GPU.
- macOS reserves a share of unified memory for the system, so not all of it is available to a model.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run SmolLM3 on Mac mini (M4 Pro, 64 GB)?
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~43.1 GB spare and running at ~36.1 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of SmolLM3 should I use on Mac mini (M4 Pro, 64 GB)?
Q8_0 — it needs about 4.9 GB of the 48 GB available, downloads as roughly 3.3 GB, and runs at an estimated 36.1 tokens/sec with up to 128K of context.
What limits SmolLM3 on Mac mini (M4 Pro, 64 GB)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
Other Computers
Other Models on Mac mini (M4 Pro, 64 GB)
- StarCoder 2 on Mac mini (M4 Pro, 64 GB)
- Aya 3B (Tiny Aya) on Mac mini (M4 Pro, 64 GB)
- VibeThinker on Mac mini (M4 Pro, 64 GB)
- Yi 1.5 Family on Mac mini (M4 Pro, 64 GB)
- Aya Expanse on Mac mini (M4 Pro, 64 GB)
SmolLM3 on GPUs
- SmolLM3 on NVIDIA GeForce RTX 5060 Ti 8GB
- SmolLM3 on NVIDIA GeForce RTX 5060
- SmolLM3 on NVIDIA GeForce RTX 4060