Apertus v1.5 8B on Apple iPhone 17 Pro / 17 Pro Max
Can the Apple iPhone 17 Pro / 17 Pro Max run Apertus v1.5 8B?
Yes, it runs well (tight fit)
Runs well (tight fit). At Q4_K_M it needs about 7.1 GB of 7.2 GB usable, with an estimated speed of 10.9 tok/s.
- Best quant
Q4_K_M- Generation speed
- 10.9 tok/s
- Prompt processing
- 216 tok/s
- First token (1K prompt)
- 4.7 s
- Largest safe context
- 8K
Download Apertus v1.5 8B
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 Apertus v1.5 8B 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 Swiss AI, for fine-tuning, vLLM, Transformers.
huggingface.co/swiss-ai/Apertus-v1.5-8B (opens in a new tab)
How it’s rated
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 quality | 19.7 / 45 | |
|---|---|---|
| Memory fit | 24.0 / 25 | |
| Speed | 7.8 / 15 | |
| Memory use | 15.0 / 15 | |
| Match score | 66.5 / 100 |
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 | 5.2 GB | 7.2 GB | 17.9 tok/s | Runs well | 32Basic quality (D) · 32 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 | 6.0 GB | 7.2 GB | 14.1 tok/s | Runs well | 38Basic quality (D) · 38 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 | 7.1 GB | 7.2 GB | 10.9 tok/s | Runs welltight fit | 43Fair quality (C) · 43 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. | |
Q5_K_M | 8.0 GB | 7.2 GB | — | Won't runover the app memory limit | 44Fair quality (C) · 44 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | |
Q6_K | 9.0 GB | 7.2 GB | — | Won't runover the app memory limit | 45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | |
Q8_0 | 11.1 GB | 7.2 GB | — | Won't runover the app memory limit | 46Fair quality (C) · 46 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking. | |
F16 | 19.5 GB | 7.2 GB | — | Won't runover the app memory limit | 46Fair quality (C) · 46 of 100: how capable the model is at F16, whatever it runs on. Match score 6/100 (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
-
46Fair quality (C) · 46 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.
NVIDIA GeForce RTX 4070 Ti SUPER
-
46Fair quality (C) · 46 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.
NVIDIA GeForce RTX 4080 SUPER
-
46Fair quality (C) · 46 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.
NVIDIA GeForce RTX 5070 Ti
-
46Fair quality (C) · 46 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.
NVIDIA GeForce RTX 5080
-
46Fair quality (C) · 46 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.
NVIDIA GeForce RTX 5090
-
45Fair quality (C) · 45 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4070 SUPER
Quick answers
How much memory does Apertus v1.5 8B need on the iPhone 17 Pro?
At Q4_K_M it needs about 7.1 GB. The iPhone 17 Pro has 7.2 GB usable.
How fast is Apertus v1.5 8B on the iPhone 17 Pro?
About 10.9 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.
Which quantization of Apertus v1.5 8B 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.