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Can the Apple iPad Pro 11-inch / 13-inch (M5) (16 GB RAM) run MiniCPM5 2B?

60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 3.6 GB of 9.9 GB usable, with an estimated speed of 39.0 tok/s.

Best quant
Q8_0
Generation speed
39.0 tok/s
Prompt processing
1,410 tok/s
First token (1K prompt)
0.7 s
Largest safe context
128K

Best app to run it on the Apple iPad Pro 11-inch / 13-inch (M5) (16 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 MiniCPM5 2B in the app

Want an agent? MiniCPM5 2B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 MiniCPM5 2B

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 MiniCPM5-2B-Q8_0.gguf for the Apple iPad Pro 11-inch / 13-inch (M5) (16 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 openbmb/MiniCPM5-2B-GGUF, open it and download MiniCPM5-2B-Q8_0.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 (M5) (16 GB RAM): Q8_0. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q8_0 file (opens in a new tab)

huggingface.co/openbmb/MiniCPM5-2B-GGUF (opens in a new tab)

ollama run hf.co/openbmb/MiniCPM5-2B-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/openbmb/MiniCPM5-2B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/OpenBMB/MiniCPM (opens in a new tab)

OpenBMB's official MiniCPM repo, with deployment guides for the MiniCPM5 series.

git clone https://github.com/OpenBMB/MiniCPM

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

How it’s rated

Quality 60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, 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 great
How well it runs on the Apple iPad Pro 11-inch / 13-inch (M5) (16 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.27.0 / 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.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.4 / 15
Match score75.2 / 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 (M5) (16 GB RAM)

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 2.0 GB 9.9 GB 126 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. —
Q3_K_M 2.2 GB 9.9 GB 99.0 tok/s Runs great 50Fair quality (C) · 50 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. —
Q4_K_M 2.5 GB 9.9 GB 76.6 tok/s Runs great 57Good quality (B) · 57 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Q4_K_M
Q5_K_M 2.7 GB 9.9 GB 63.3 tok/s Runs great 58Good quality (B) · 58 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. —
Q6_K 3.0 GB 9.9 GB 53.1 tok/s Runs great 59Good quality (B) · 59 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. —
Q8_0 3.6 GB 9.9 GB 39.0 tok/s Runs great 60Good quality (B) · 60 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. Q8_0
F16 6.0 GB 9.9 GB 18.0 tok/s Runs well 60Good quality (B) · 60 of 100: how capable the model is at F16, whatever it runs on. Match score 75/100 (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 MiniCPM5 2B need on the iPad Pro 11-inch (M5) 16GB?

At Q8_0 it needs about 3.6 GB. The iPad Pro 11-inch (M5) 16GB has 9.9 GB usable.

How fast is MiniCPM5 2B on the iPad Pro 11-inch (M5) 16GB?

About 39.0 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of MiniCPM5 2B should I use on the iPad Pro 11-inch (M5) 16GB?

Q8_0. 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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