Can I Run SmolLM3 on MacBook Pro 14" (M4 Pro, 24 GB)?
Written by Jakub Rusinowski · Last updated September 6, 2026
Yes — comfortably
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving ~13.1 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
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MacBook Pro 14" (M4 Pro, 24 GB) — what it gives a model
| Usable memory for models | 18 GB |
| Memory bandwidth | 273 GB/s |
| Form factor | Laptop |
| Operating system | macOS |
| Memory upgradeable | No — soldered |
| Price | $1,999 (lib/data/laptops.ts (street price), checked 2026-07-06) |
SmolLM3 on MacBook Pro 14" (M4 Pro, 24 GB): memory by quantization
| Quant | Memory needed | Fits 18 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 7.8 GB | ✓ Yes | 64K | ~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.
MacBook Pro M4 Pro 24 GB limitations
- Unified memory is soldered and cannot be upgraded after purchase.
- 24 GB is the practical ceiling for a single mid-size model plus a long context.
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.
- 24 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 MacBook Pro 14" (M4 Pro, 24 GB)?
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 18 GB usable on MacBook Pro 14" (M4 Pro, 24 GB), leaving ~13.1 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 MacBook Pro 14" (M4 Pro, 24 GB)?
Q8_0 — it needs about 4.9 GB of the 18 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 MacBook Pro 14" (M4 Pro, 24 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
- SmolLM3 on MacBook Pro M4 Max 128 GB
- SmolLM3 on MacBook Pro M4 Max 48 GB
- SmolLM3 on MacBook Air M4 16 GB
Other Models on MacBook Pro 14" (M4 Pro, 24 GB)
- StarCoder 2 on MacBook Pro 14" (M4 Pro, 24 GB)
- Aya 3B (Tiny Aya) on MacBook Pro 14" (M4 Pro, 24 GB)
- VibeThinker on MacBook Pro 14" (M4 Pro, 24 GB)
- Yi 1.5 Family on MacBook Pro 14" (M4 Pro, 24 GB)
- Aya Expanse on MacBook Pro 14" (M4 Pro, 24 GB)
SmolLM3 on GPUs
- SmolLM3 on NVIDIA GeForce RTX 5060 Ti 8GB
- SmolLM3 on NVIDIA GeForce RTX 5060
- SmolLM3 on NVIDIA GeForce RTX 4060