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Can the Apple iPhone 17 Pro / 17 Pro Max run Apertus 8B Instruct?

33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q3_K_M it needs about 5.6 GB of 7.2 GB usable, with an estimated speed of 15.6 tok/s.

Best quant
Q3_K_M
Generation speed
15.6 tok/s
Prompt processing
239 tok/s
First token (1K prompt)
4.3 s
Largest safe context
19K

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 Apertus 8B Instruct 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 Apertus 8B Instruct

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 Apertus-8B-Instruct-2509-Q3_K_M.gguf for the Apple iPhone 17 Pro / 17 Pro Max.

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/Apertus-8B-Instruct-2509-GGUF, open it and download Apertus-8B-Instruct-2509-Q3_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 iPhone 17 Pro / 17 Pro Max: Q3_K_M. 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 Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/Apertus-8B-Instruct-2509-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Apertus-8B-Instruct-2509-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/swiss-ai/Apertus-8B-Instruct-2509 (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 well
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.15.1 / 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.9.4 / 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 score64.5 / 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 iPhone 17 Pro / 17 Pro Max

Quant Memory needed Available Speed Runs Quality File to pick in the app
Q2_K 4.9 GB 7.2 GB 19.8 tok/s Runs well 28Basic quality (D) · 28 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. Q2_K
Q3_K_M 5.6 GB 7.2 GB 15.6 tok/s Runs well 33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. Q3_K_M
Q4_K_M 6.6 GB 7.2 GB 12.0 tok/s Runs welltight fit 38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. Q4_K_M
Q5_K_M 7.4 GB 7.2 GB — Won't runover the app memory limit 38Basic quality (D) · 38 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. Q5_K_M
Q6_K 8.3 GB 7.2 GB — Won't runover the app memory limit 39Basic quality (D) · 39 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. Q6_K
Q8_0 10.2 GB 7.2 GB — Won't runover the app memory limit 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. Q8_0
F16 17.8 GB 7.2 GB — Won't runover the app memory limit 40Fair quality (C) · 40 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. BF16

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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