Phi-4 Multimodal Instruct on Apple iPhone 17 Pro / 17 Pro Max

Can the Apple iPhone 17 Pro / 17 Pro Max run Phi-4 Multimodal Instruct?

Yes, it runs well

Runs well. At Q4_K_M it needs about 5.1 GB of 7.2 GB usable, with an estimated speed of 17.4 tok/s.

Best quant: Q4_K_M
Generation speed: 17.4 tok/s
Prompt processing: 345 tok/s
First token (1K prompt): 3.0 s
Largest safe context: 23K

Download Phi-4 Multimodal 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.

On iPhone or iPad

No mobile app in the App Store runs Phi-4 Multimodal Instruct right now: mobile apps need a GGUF version, and none is published. Mobile apps · models that run on mobile devices.

Files and commands for a computer

Original weights · Hugging Face

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

huggingface.co/microsoft/Phi-4-multimodal-instruct (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (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 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.	18.8 / 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.	9.9 / 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 score	68.7 / 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`	3.9 GB	7.2 GB	28.6 tok/s	Runs well	30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q3_K_M`	4.4 GB	7.2 GB	22.5 tok/s	Runs well	36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q4_K_M`	5.1 GB	7.2 GB	17.4 tok/s	Runs well	41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q5_K_M`	5.6 GB	7.2 GB	14.4 tok/s	Runs well	42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q6_K`	6.2 GB	7.2 GB	12.1 tok/s	Runs welltight fit	43Fair quality (C) · 43 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking.	—
`Q8_0`	7.6 GB	7.2 GB	—	Won't runover the app memory limit	44Fair quality (C) · 44 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.	—
`F16`	12.8 GB	7.2 GB	—	Won't runover the app memory limit	44Fair quality (C) · 44 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.	—

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

Other hardware that runs it

44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB Q8_0 · 40.4 tok/s · Runs great
44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER Q8_0 · 56.5 tok/s · Runs great
44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 Q8_0 · 39.1 tok/s · Runs great
43Fair quality (C) · 43 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 Q6_K · 41.6 tok/s · Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX F16 · 42.7 tok/s · Runs great
44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 F16 · 48.4 tok/s · Runs great

Quick answers

How much memory does Phi-4 Multimodal Instruct need on the iPhone 17 Pro?

At Q4_K_M it needs about 5.1 GB. The iPhone 17 Pro has 7.2 GB usable.

How fast is Phi-4 Multimodal Instruct on the iPhone 17 Pro?

About 17.4 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Phi-4 Multimodal Instruct should I use on the iPhone 17 Pro?

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.