Can I Run SmolLM2 on Mac mini (M4 Pro, 64 GB)?
Written by Jakub Rusinowski · Last updated November 20, 2024
Yes — comfortably
Yes, comfortably — SmolLM2 1.7B Instruct at Q8_0 needs about 4.2 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~43.8 GB spare and running at ~48.7 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~48.7 tok/s
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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 |
SmolLM2 on Mac mini (M4 Pro, 64 GB): memory by quantization
| Quant | Memory needed | Fits 48 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 5.8 GB | ✓ Yes | 8K | ~32 tok/s | 3.4 GB |
| Q8_0 | 4.2 GB | ✓ Yes | 8K | ~48.7 tok/s | 1.8 GB |
| Q6_K | 3.8 GB | ✓ Yes | 8K | ~56.3 tok/s | 1.4 GB |
| Q5_K_M | 3.6 GB | ✓ Yes | 8K | ~60.7 tok/s | 1.2 GB |
| Q4_K_M | 3.4 GB | ✓ Yes | 8K | ~65.4 tok/s | 1 GB |
| Q3_K_M | 3.1 GB | ✓ Yes | 8K | ~75.5 tok/s | 0.7 GB |
| Q2_K | 3 GB | ✓ Yes | 8K | ~82.4 tok/s | 0.6 GB |
Which SmolLM2 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| SmolLM2 1.7B Instruct | 3.4 GB | ✓ Fits | ~65.4 tok/s |
| SmolLM2 360M Instruct | 1.4 GB | ✓ Fits | ~179.2 tok/s |
What to watch out for
- 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 computed from this model's published attention configuration.
FAQ
Can I run SmolLM2 on Mac mini (M4 Pro, 64 GB)?
Yes, comfortably — SmolLM2 1.7B Instruct at Q8_0 needs about 4.2 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~43.8 GB spare and running at ~48.7 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of SmolLM2 should I use on Mac mini (M4 Pro, 64 GB)?
Q8_0 — it needs about 4.2 GB of the 48 GB available, downloads as roughly 1.8 GB, and runs at an estimated 48.7 tokens/sec with up to 8K of context.
What limits SmolLM2 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)
- SmolLM3 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)
SmolLM2 on GPUs
- SmolLM2 on NVIDIA GeForce RTX 5060 Ti 8GB
- SmolLM2 on NVIDIA GeForce RTX 5060
- SmolLM2 on NVIDIA GeForce RTX 4060