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Can the Apple iPhone 17 Pro / 17 Pro Max run PaddleOCR-VL 1.6 0.9B?

42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 1.8 GB of 7.2 GB usable, with an estimated speed of 51.4 tok/s.

Best quant
Q8_0
Generation speed
51.4 tok/s
Prompt processing
2,004 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the Apple iPhone 17 Pro / 17 Pro Max

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 PaddleOCR-VL 1.6 0.9B 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 PaddleOCR-VL 1.6 0.9B

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.

On iPhone or iPad

No mobile app in the App Store runs PaddleOCR-VL 1.6 0.9B right now: mobile apps open a model as one file, and its GGUF files that fit come split into parts. Mobile apps · models that run on mobile devices.

Files and commands for a computer

Quantized files · GGUF

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

huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6-GGUF (opens in a new tab)

ollama run hf.co/PaddlePaddle/PaddleOCR-VL-1.6-GGUF

Original weights · Hugging Face

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

huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/PaddlePaddle/PaddleOCR (opens in a new tab)

git clone https://github.com/PaddlePaddle/PaddleOCR

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (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 iPhone 17 Pro / 17 Pro Max: 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.18.9 / 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.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.5.7 / 15
Match score64.6 / 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 iPhone 17 Pro / 17 Pro Max

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 1.1 GB 7.2 GB 166 tok/s Runs great 29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. —
Q3_K_M 1.2 GB 7.2 GB 130 tok/s Runs great 35Basic quality (D) · 35 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. —
Q4_K_M 1.3 GB 7.2 GB 101 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. —
Q5_K_M 1.4 GB 7.2 GB 83.4 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. —
Q6_K 1.5 GB 7.2 GB 70.0 tok/s Runs great 41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. —
Q8_0 1.8 GB 7.2 GB 51.4 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. —
F16 2.7 GB 7.2 GB 23.7 tok/s Runs well 42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. —

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.

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