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Can the Apple M2 (8 GB) run Qwen3 4B Instruct 2507?

74 ptsGreat (A) · 74 of 100 points: Model quality 23.1/45, Memory fit 24.0/25, Speed 12.2/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 4.7 GB of 5.4 GB usable, with an estimated speed of 26.3 tok/s.

Best quant
Q4_K_M
Generation speed
26.3 tok/s
Prompt processing
555 tok/s
First token (1K prompt)
1.8 s
Largest safe context
12K

Download Qwen3 4B Instruct 2507

Recommended for the Apple M2 (8 GB): Q4_K_M. 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 qwen3:4b-instruct-2507-q4_K_M

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 Q4_K_M file (opens in a new tab)

huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-4B-Instruct-2507 (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 (opens in a new tab)

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

Qwen’s main repo, renamed from Qwen2.5 to Qwen3; it still documents Qwen2.5.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A 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.23.1 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total74.2 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 23.1/45, Memory fit 24.0/25, Speed 12.2/15, Memory use 15.0/15

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

Every quantization on the Apple M2 (8 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 3.8 GB 5.4 GB 43.2 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 17.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q3_K_M 4.2 GB 5.4 GB 34.0 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 20.4/45, Memory fit 25.0/25, Speed 13.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 4.7 GB 5.4 GB 26.3 tok/s Tight fit 74 ptsGreat (A) · 74 of 100 points: Model quality 23.1/45, Memory fit 24.0/25, Speed 12.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 5.1 GB 5.4 GB 21.7 tok/s Tight fit 73 ptsGreat (A) · 73 of 100 points: Model quality 23.6/45, Memory fit 24.0/25, Speed 11.1/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 5.5 GB 5.4 GB 15.5 tok/s Runs with offload 58 ptsGood (B) · 58 of 100 points: Model quality 24.1/45, Memory fit 10.0/25, Speed 9.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 6.5 GB 5.4 GB 11.4 tok/s Heavy offload 51 ptsUsable (C) · 51 of 100 points: Model quality 24.3/45, Memory fit 4.0/25, Speed 8.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 10.3 GB 5.4 GB — Won't run 7 ptsWon't run (F) · 7 of 100 points: Model quality 7.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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

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.