Can the Apple M2 Pro (32 GB) run Qwen3.5 0.8B?

56 pts

Yes, it runs well

Runs well. At F16 it needs about 4.2 GB of 21.4 GB usable, with an estimated speed of 57.0 tok/s.

Best quant: F16
Generation speed: 57.0 tok/s
Prompt processing: 4,846 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 256K

Download Qwen3.5 0.8B

Recommended for the Apple M2 Pro (32 GB): F16. 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 qwen3.5:0.8b-bf16

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.

Download the F16 file (opens in a new tab)

huggingface.co/unsloth/Qwen3.5-0.8B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.5-0.8B-GGUF:BF16

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.5-0.8B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/QwenLM/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

Links checked 7 days 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.	11.7 / 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.	4.5 / 15
Total	56.2 / 100	56 ptsUsable (C) · 56 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.5/15

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`	2.8 GB	21.4 GB	399 tok/s	Runs well	51 ptsUsable (C) · 51 of 100 points: Model quality 8.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.0/15	GGUF (UD-Q2_K_XL) (opens in a new tab)
`Q3_K_M`	2.8 GB	21.4 GB	314 tok/s	Runs well	52 ptsUsable (C) · 52 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.1/15	GGUF (opens in a new tab)
`Q4_K_M`	2.9 GB	21.4 GB	243 tok/s	Runs well	54 ptsUsable (C) · 54 of 100 points: Model quality 11.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.2/15	GGUF (opens in a new tab)
`Q5_K_M`	3.0 GB	21.4 GB	201 tok/s	Runs well	54 ptsUsable (C) · 54 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.3/15	GGUF (opens in a new tab)
`Q6_K`	3.1 GB	21.4 GB	169 tok/s	Runs well	54 ptsUsable (C) · 54 of 100 points: Model quality 11.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.4/15	GGUF (opens in a new tab)
`Q8_0`	3.3 GB	21.4 GB	124 tok/s	Runs well	55 ptsUsable (C) · 55 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.6/15	GGUF (opens in a new tab)
`F16`	4.2 GB	21.4 GB	57.0 tok/s	Runs well	56 ptsUsable (C) · 56 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.5/15	GGUF (opens in a new tab) 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

59 ptsGood (B) · 59 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.2/15 NVIDIA GeForce RTX 4060 F16 · 90.0 tok/s
57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 Intel Arc B580 F16 · 115 tok/s
57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 NVIDIA GeForce RTX 3060 12GB F16 · 119 tok/s
57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 NVIDIA GeForce RTX 4070 SUPER F16 · 167 tok/s
57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 Apple M4 Pro (24 GB) F16 · 77.8 tok/s
57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 Apple M1 (16 GB) Q8_0 · 42.2 tok/s

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.