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Can the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM) run Qwen3 4B Thinking 2507?

53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q4_K_M it needs about 4.3 GB of 4.4 GB usable, with an estimated speed of 37.7 tok/s.

Best quant
Q4_K_M
Generation speed
37.7 tok/s
Prompt processing
792 tok/s
First token (1K prompt)
1.3 s
Largest safe context
8K

Best app to run it on the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM)

On iOS

PocketPal AI

Mobile app · runs on llama.cpp

  • Runs the model on the device itself, offline: download it once in the app.

Get it on the App Store Then download Qwen3 4B Thinking 2507 in the app

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Qwen3 4B Thinking 2507

On a phone, tablet or other mobile device you install a chat app and get the model file through it, so there are no commands to copy. Use Qwen3-4B-Thinking-2507-Q4_K_M.gguf for the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM).

On iPhone or iPad

PocketPal AI

Free mobile app · runs the model on the device with llama.cpp

Get it on the App Store

  1. Install PocketPal AI (free) and open it.
  2. Tap the menu (☰), then Models, then + and Add from Hugging Face.
  3. Search for unsloth/Qwen3-4B-Thinking-2507-GGUF, open it and download Qwen3-4B-Thinking-2507-Q4_K_M.gguf.
  4. Tap Load next to the model and start a chat. It runs on the device, with no connection needed from then on.
Files and commands for a computer

Recommended for the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM): 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.

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

ollama run qwen3:4b-thinking-2507-q4_K_M

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-Thinking-2507-GGUF (opens in a new tab)

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

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-4B-Thinking-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)

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

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

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welltight fit
How well it runs on the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.23.9 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.6 / 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
Match score77.5 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Apple iPad Pro 11-inch / 13-inch (M4) (8 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 3.4 GB 4.4 GB 61.9 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. Q2_K
Q3_K_M 3.8 GB 4.4 GB 48.7 tok/s Runs welltight fit 47Fair quality (C) · 47 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Q3_K_M
Q4_K_M 4.3 GB 4.4 GB 37.7 tok/s Runs welltight fit 53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. Q4_K_M
Q5_K_M 4.7 GB 4.4 GB — Won't runover the app memory limit 54Fair quality (C) · 54 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. Q5_K_M
Q6_K 5.1 GB 4.4 GB — Won't runover the app memory limit 55Good quality (B) · 55 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. Q6_K
Q8_0 6.1 GB 4.4 GB — Won't runover the app memory limit 56Good quality (B) · 56 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. Q8_0
F16 9.8 GB 4.4 GB — Won't runover the app memory limit 56Good quality (B) · 56 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. F16

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

Other hardware that runs it

Quick answers

How much memory does Qwen3 4B Thinking 2507 need on the iPad Pro 11-inch (M4) 8GB?

At Q4_K_M it needs about 4.3 GB. The iPad Pro 11-inch (M4) 8GB has 4.4 GB usable.

How fast is Qwen3 4B Thinking 2507 on the iPad Pro 11-inch (M4) 8GB?

About 37.7 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Qwen3 4B Thinking 2507 should I use on the iPad Pro 11-inch (M4) 8GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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