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Can the Apple M2 Pro (32 GB) run SmolLM2 1.7B Instruct?

55 ptsUsable (C) · 55 of 100 points: Model quality 9.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15

Yes, it runs well

Runs well. At Q8_0 it needs about 5.7 GB of 21.4 GB usable, with an estimated speed of 62.9 tok/s.

Best quant
Q8_0
Generation speed
62.9 tok/s
Prompt processing
2,465 tok/s
First token (1K prompt)
0.4 s
Largest safe context
8K

Download SmolLM2 1.7B Instruct

Recommended for the Apple M2 Pro (32 GB): Q8_0. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run smollm2:1.7b-instruct-q8_0

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF

Original weights · Hugging Face

Full-precision files from Hugging Face, for fine-tuning, vLLM, Transformers.

huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Hugging Face. The weights themselves download from Hugging Face.

github.com/huggingface/smollm (opens in a new tab)

git clone https://github.com/huggingface/smollm

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

How the C grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.9.0 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.6.2 / 15
Total55.2 / 10055 ptsUsable (C) · 55 of 100 points: Model quality 9.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 4.6 GB 21.4 GB 203 tok/s Runs well 51 ptsUsable (C) · 51 of 100 points: Model quality 6.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.0/15
Q3_K_M 4.8 GB 21.4 GB 160 tok/s Runs well 52 ptsUsable (C) · 52 of 100 points: Model quality 7.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.1/15
Q4_K_M 5.0 GB 21.4 GB 124 tok/s Runs well 53 ptsUsable (C) · 53 of 100 points: Model quality 8.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 GGUF (opens in a new tab)
Q5_K_M 5.1 GB 21.4 GB 102 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 8.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15
Q6_K 5.3 GB 21.4 GB 85.8 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 8.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.7/15
Q8_0 5.7 GB 21.4 GB 62.9 tok/s Runs well 55 ptsUsable (C) · 55 of 100 points: Model quality 9.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15
F16 7.3 GB 21.4 GB 29.0 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 9.0/45, Memory fit 25.0/25, Speed 12.8/15, Memory use 7.9/15 Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.