AI Models for iPhone 14 — What runs on 6GB

14 great · 11 slow · 68 won't fit
Chip
Apple A15 Bionic
Memory bandwidth
34.1 GB/s
NPU
15.8 TOPS
RAM options
6 GB
Usable for models
~3.9 GB
Year
2022

Specs checked against manufacturer and public documentation on .

What runs on the iPhone 14

All 93 models at their recommended quant, on the 6GB configuration. Select any row for the full report.

ModelParamsQuantNeedsSpeedVerdict

~ = bandwidth-based estimate · ✓ = measured on real hardware

Best model by use case

Best for Chat

Top everyday assistant & writing pick here — ~25.6 tokens/s at Q8_0, using 1.3 of ~3.9GB.

Best for Coding

Top code completion & explain-this pick here — ~8.1 tokens/s at Q4_K_M, using 2.8 of ~3.9GB.

Best for Reasoning

Top math & step-by-step thinking pick here — ~14 tokens/s at Q4_K_M, using 1.9 of ~3.9GB.

FAQ

What is the biggest AI model the iPhone 14 can run?

Ternary Bonsai 8B (8B parameters) at Q2_0 — it needs 3.5GB of the ~3.9GB usable on the 6GB iPhone 14, at ~7 tokens/s.

How much of the iPhone 14's 6GB RAM can AI models actually use?

About 3.9GB. iOS caps a single app at roughly 65% of total RAM, so of the 6GB about 3.9GB is actually available to a model.

Can the iPhone 14 run Llama 3.1 8B?

Not at Q4_K_M: it needs 6.3GB but the iPhone 14 only has ~3.9GB usable. Try a smaller model like Qwen3 0.6B.

How fast is local AI on the iPhone 14?

The Apple A15 Bionic has 34.1GB/s of memory bandwidth, which is what decode speed scales with. Small models like Ternary Bonsai 1.7B reach ~30.7 tokens/s; larger 7–14B models land in the single digits. Anything above ~8 tokens/s feels smooth for chat.

Which quantization should I use on the iPhone 14?

Q4_K_M is the size/quality sweet spot for most models. For example, Qwen3 0.6B at Q8_0 takes 1.3GB of memory here. Only drop to Q3 or IQ4 if a model just misses fitting; Q8 rarely pays off on 6GB of RAM.

Is 6GB of RAM enough for local AI?

25 of the 93 models we track fit on the iPhone 14 — 14 run great and 11 run with compromises. 68 models (mostly 12B+) don't fit at their recommended quant.

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